At present, in the analysis and synthesis of complex (large) systems, a systematic approach has been developed, which differs from the classical (or inductive) approach. Classic approach considers the system by moving from the particular to the general and synthesizes (constructs) the system by merging its components, developed separately. In contrast to this systems approach involves a consistent transition from the general to the particular, when the consideration is based on the goal, and the object under study stands out from the environment.
Simulation object. Specialists in the design and operation of complex systems deal with control systems of various levels that have a common property - the desire to achieve some goal. This feature will be taken into account in the following definitions of the system.
System or object S- purposeful set of interconnected elements of any nature.
External environment E- a set of elements of any nature existing outside the system that influence the system or are under its influence.
Depending on the purpose of the study, different relationships between the object S itself and the environment E can be considered. Thus, depending on the level at which the observer is located, the object of study can be distinguished in different ways and various interactions of this object with the environment can take place.
With the development of science and technology, the object itself is continuously becoming more complex, and even now they are talking about the object of study as some kind of complex system that consists of various components interconnected with each other. Therefore, considering the system approach as the basis for building large systems and as the basis for creating a methodology for their analysis and synthesis, it is first of all necessary to define the very concept of the system approach.
Systems approach is an element of the doctrine general laws development of nature and one of the expressions of the dialectical doctrine. With a systematic approach to modeling systems, it is necessary first of all to clearly define the purpose of modeling. Since it is impossible to completely model a really functioning system (the original system, or the first system), a model (model system, or the second system) is created for the problem posed.
Thus, in relation to modeling issues, the goal arises from the required modeling tasks, which allows you to approach the choice of criterion and evaluate which elements will be included in the created model M. Therefore, it is necessary to have a criterion for selecting individual elements in the created model.
Systems Research Approaches. Important for a systematic approach is the definition system structure- a set of links between the elements of the system, reflecting their interaction. Structure systems can be studied
1. from outside in terms of the composition of individual subsystems and the relationships between them,
2. well from within, when individual properties are analyzed that allow the system to achieve a given goal, that is, when the functions of the system are studied.
In accordance with this, a number of approaches to the study of the structure of the system with its properties have been outlined, which should primarily include structural approach and functional approach.
At structural approach the composition of the selected elements of the system S and the connections between them are revealed. The totality of elements and links between them makes it possible to judge the structure of the system. The latter, depending on the purpose of the study, can be described in different levels consideration. The most general description of the structure is a topological description, which makes it possible to define the constituent parts of the system in the most general terms and is well formalized on the basis of graph theory.
Less common is functional description , when individual functions are considered, i.e., algorithms for the behavior of the system, and the functional approach, which evaluates the functions that the system performs, and the function is understood as a property that leads to the achievement of the goal. Since the function displays a property, and the property displays the interaction of the system S with the external environment E, the properties can be expressed as either some characteristics of the elements and subsystems of the system, or the system S as a whole. If there is some standard of comparison, you can enter quantitative and qualitative characteristics of systems. For a quantitative characteristic, numbers are entered that express the relationship between this characteristic and the standard. The qualitative characteristics of the system are found, for example, using the method of expert assessments.
The manifestation of system functions in time S(t), i.e., the functioning of the system, means the transition of the system from one state to another, i.e., movement in the state space Z.
The systems approach has been used in systems engineering due to the need to study large real systems, when the insufficiency, and sometimes the error, of making any particular decisions has affected. The emergence of a systematic approach was influenced by an increasing amount of initial data during development, the need to take into account complex stochastic relationships in the system and the effects of the external environment E. All this forced researchers to study a complex object not in isolation, but in interaction with the external environment, as well as in conjunction with other systems of some metasystems. A systematic approach allows solving the problem of building a complex system, taking into account all factors and opportunities proportional to their significance, at all stages of studying the system S and building a model M.
The systems approach means that each system S is an integrated whole even when it consists of separate disparate subsystems. Thus, the system approach is based on the consideration of the system as an integrated whole, and this consideration during development begins with the main thing - the formulation of the goal of functioning.
The process of synthesizing the model M on the basis of a systematic approach is conventionally presented in fig. b. Based on the initial data D, which are known from the analysis of the external system, those restrictions that are imposed on the system from above or based on the possibilities of its implementation, and on the basis of the purpose of functioning, the initial requirements are formulated T to the system model S. On the basis of these requirements some subsystems are formed approximately P, elements E and the most difficult stage of synthesis is carried out - the choice AT components of the system, for which special criteria for selecting HF are used. When modeling, it is necessary to ensure maximum efficiency of the system model.
Efficiency is usually defined as some difference between some indicators of the value of the results obtained as a result of the operation of the model and the costs that were invested in its development and creation.
When modeling systems, two approaches are used: classical (inductive), historically the first, and systemic, which has been developed recently.
Classic approach. Historically, the classical approach to the study of the object, the modeling of the system, was the first to develop. The classical approach to the synthesis of the model (M) of the system is shown in fig. 3. The real object to be modeled is divided into subsystems, initial data (D) for modeling are selected and goals (T) are set, reflecting certain aspects of the modeling process. Based on a separate set of initial data, the goal is to model a separate aspect of the functioning of the system; on the basis of this goal, a certain component (K) of the future model is formed. The set of components is combined into a model.
That. components are summed, each component solves its own tasks and is isolated from other parts of the model. We apply the approach only for simple systems, where it is possible to ignore the relationship between components. Two distinctive aspects of the classical approach can be noted:
1. there is a movement from the particular to the general when creating a model;
2. The created model (system) is formed by summing up its individual components and does not take into account the emergence of a new systemic effect.
Rice. 3. Classical approach to building an object, studying the model
Systems approach - a methodological concept based on the desire to build a complete picture of the object under study, taking into account the elements of the object that are important for the problem being solved, the links between them and external links with other objects and the environment. With the complication of modeling objects, it became necessary to observe them with more high level. In this case, the developer considers this system as some subsystem of a higher rank. For example, if the task is to design a monitoring system for a separate object, then from the standpoint of a systematic approach, we must not forget that this system is integral part some cremplex. The system approach is based on the consideration of the system as an integrated whole, and this consideration during development begins with the main thing - the formulation of the goal of functioning. On fig. 4. The process of synthesizing a system model based on a systematic approach is conditionally presented. Important for the system approach is the definition of the structure of the system - the totality of links between the elements of the system, reflecting their interaction.
Rice. 4. A systematic approach to building an object, studying a model
There are structural and functional approaches to studying the structure of a system and its properties. With a structural approach, the composition of the selected elements of the system and the links between them are revealed. With the functional approach, algorithms of the system behavior are considered (functions are properties that lead to the achievement of the goal).
test questions to section 2
1. What is determined in the process of system analysis?
2. What is determined in the process of system synthesis?
3. How is the effectiveness of the system evaluated?
4. What is meant by an optimal system?
5. Properties inherent in a complex system, and their brief description.
6. What is the problem of choosing the level of detail of models?
7. List the main stages of system modeling.
Classical(or inductive) approach to modeling considers the system, moving from the particular to the general, and synthesizes it by merging the components developed separately. Systems approach involves a consistent transition from the general to the particular, when the consideration is based on the goal, while the object is distinguished from the surrounding world.
When creating a new object with useful properties(for example, control systems) are given criteria determining the degree of usefulness of the obtained properties. Since any modeling object is a system of interrelated elements, we introduce the concept of a system. System S there is a purposeful set of interconnected elements of any nature. External environment. E is a set of elements of any nature existing outside the system that influence the system or are under its influence.
With a systematic approach to modeling, first of all, the purpose of modeling is clearly defined. Creating a model of a complete analogue of the original is laborious and expensive, so the model is created for a specific purpose.
Important for a systematic approach is the definition system structure- a set of links between the elements of the system, reflecting their interaction. There are a number of approaches to the study of systems and its properties, which include structural and functional. At structural approach the composition of the selected elements of the system is revealed S and connections between them. The totality of elements and connections makes it possible to judge the properties of the selected part of the system. At functional approach functions (algorithms) of the system behavior are considered, and each function describes the behavior of one property under external influence E. This approach does not require knowledge of the structure of the system, and its description consists of a set of functions of its response to external influences.
The classical method of building a model uses a functional approach, in which the element of the model is taken component, describing the behavior of one property and not reflecting the real composition of the elements. In addition, the components of the system are isolated from each other, which poorly reflects the modeled system. This method of building a model is applicable only for simple systems, since it requires the inclusion of functions that describe the properties of the system, relationships between properties that may be poorly defined or unknown.
With the complication of the simulated systems, when it is impossible to take into account all the mutual influences of properties, it is applied system method, based on a structural approach. At the same time, the system S broken down into a number of subsystems S l with their own properties, which, of course, are easier to describe by functional dependencies, and links between subsystems are determined. In this case, the system functions in accordance with the properties of individual subsystems and the connections between them. This eliminates the need to describe the functional relationship between the properties of the system S, makes the model more flexible, since changing the properties of one of the subsystems automatically changes the properties of the system.
Classification of types of modeling
Depending on the nature of the studied processes in the system S and the purpose of modeling, there are many types of models and ways to classify them, for example, according to the purpose of use, the presence of random effects, relation to time, feasibility, scope, etc. (Table 14).
Table 14. Model types
By purpose of use models are classified into science Experiment, in which the study of the model is carried out using various means of obtaining data about the object, the possibility of influencing the course of the process, in order to obtain new data about the object or phenomenon; comprehensive testing and production experiment, using a full-scale test of a physical object to obtain high reliability about its characteristics; optimization, related to finding the optimal indicators of the system (for example, finding the minimum cost or determining the maximum profit).
According to the presence of influences per model system are divided into deterministic(there are no random effects in the systems) and stochastic(there are probabilistic influences in the systems). The same models are classified by some authors according to the method of parameter estimation systems: in deterministic systems, model parameters are estimated by one indicator for specific values of their initial data; in stochastic systems, the presence of probabilistic characteristics of the initial data allows us to evaluate the parameters of the system by several indicators.
Relative to time models are divided into static, describing the system at a certain point in time, and dynamic, considering the behavior of the system in time. In turn, dynamic models are subdivided into discrete, in which all events occur in time intervals, and continuous, where all events occur continuously in time.
Implementation possible models are classified as mental, describing a system that is difficult or impossible to simulate realistically, real, in which the system model is represented either by a real object or its part, and information, realizing information processes (emergence, transmission, processing and use of information) on a computer. In turn, mental models are divided into visual(in which the simulated processes and phenomena proceed visually); symbolic(the system model represents a logical object in which the main properties and relations of a real object are expressed by a system of signs or symbols) and mathematical(they represent systems of mathematical objects that allow obtaining the studied characteristics of a real object). Real models are divided into natural(conducting a study on a real object and subsequent processing of the results of the experiment using the theory of similarity) and physical(conducting research on installations that preserve the nature of the phenomenon and have a physical similarity).
By area of application models are divided into universal, intended for use by many systems, and specialized, designed to study a specific system.
Mathematical models
Most milestone when building a model, there is a transition from a meaningful description to a formal one, which is explained by the participation at this stage of specialists in the subject area where the modeled system exists, and specialists in the field of system modeling. The most convenient language for their communication, the purpose of which is to build an adequate model of the system, is usually the language of mathematical descriptions. The mathematical description of the system is compact and convenient for further implementations on a computer, in order to conduct statistical tests,
Examples of building dynamic models
When modeling continuous dynamic objects, the models are usually differential Equations, relating the behavior of an object over time. positive property differential equations is that the same equation models systems of different physical nature.
The independent variable in dynamical systems is usually time, on which the unknown values of the desired function depend, which determine the behavior of the object. Mathematical description of the model in general:
where are n-dimensional vectors and is continuous.
For example, the process of small oscillations of a pendulum is described by an ordinary differential equation
.
Process in an electrical oscillating circuit 
.
Obviously, if we put
We obtain an equation describing the state in time of both systems
A general mathematical model allows you to explore one system while simulating the operation of another.
Models of dynamic systems based on differential equations have found wide application in the theory of control of various technical objects. Under the influence of previously unknown perturbations, the actual behavior of the system deviates from the desired one, set by the algorithm, and in order to approximate its behavior to the required value, automatic control of the system is introduced into the system. It can be built into the system itself, but in simulation, the control unit is separated from the system itself. In general, the structure of the multidimensional automatic control system (ACS) is shown in Fig. 3.
Figure 3. The structure of a multidimensional automatic control system.
Information Models
Information Models in many cases rely on mathematical models, since when solving problems, the mathematical model of the object, process or phenomenon under study is inevitably transformed into an information model for its implementation on a computer. Let us define the basic concepts of the information model.
Information object is a description of a real object, process or phenomenon in the form of a set of its characteristics (information elements), called details. An information object of a certain structure (property composition) forms type (class), which is assigned a unique name. An information object with specific characteristics is called instance. Each instance is identified by a job key attribute (key). The same details in different information objects can be both key and descriptive. An information object can have multiple keys.
Example. The STUDENT information object has the requisite composition: room(the record book number is a key attribute), surname, name, patronymic, date of birth, code of place of study. Information object PERSONAL MATTER: student number, home address, high school diploma number, marital status, children. The information object PLACE OF TRAINING includes the following details: the code(key props), name of the university, faculty, group. Information object TEACHER: the code(key props), Department, surname, name, patronymic, academic degree, academic title, position.
Relations, existing between real objects are defined in information models as connections. There are three types of connections: one to one (1:1), one to many(1:∞) and many to many(∞: ∞).
Connection one to one specifies that one instance of information object X corresponds to no more than one instance of information object Y, and vice versa.
Example. Information objects STUDENT and PERSONAL CASE will be connected by the relationship one to one. Each student has certain unique data in the personal file.
When in touch one to many one instance of information object X can correspond to any number of instances of information object Y, but each instance of object Y is associated with at most one instance of object X.
Example. It is necessary to establish a connection between the information objects PLACE OF TRAINING and STUDENT one to many. The same place of study can be repeated many times for different students.
Connection many to many implies that one instance of information object X corresponds to any number of instances of object Y, and vice versa.
Example. Information objects STUDENT and TEACHER have a connection many to many. Each student learns from many teachers, and each teacher teaches many students.
Examples of Information Models
Let's define an information model as a connected set of information objects that describe information processes in the subject area under study. We will divide the existing information models into universal and specialized ones. Universal models are designed for use in various subject areas, these include: Database and database management systems, automated systems management, knowledge bases, expert systems. Specialized models are designed to describe specific systems, are unique in their capabilities, and are more expensive.
Universal models.
Database
Database represent a related set of structured data related to a particular process or phenomenon in a particular subject area.
Database management system is a software package for creating, organizing the necessary processing, storage and transfer of databases.
The core of any database is data representation model. The data model represents a set of data structures and the relationships between them.
Distinguish hierarchical, network and relational data models. The hierarchical model represents relationships between objects (data) in the form of a tree.
The main concepts of the hierarchical model include:
node- a set of data attributes describing the object;
connection- a line connecting the nodes of the lower level with one node of the higher level. In this case, the node of the higher level is called ancestor for the corresponding lower-level nodes, in turn, the lower-level nodes are called descendants the overlying node associated with them (for example, in Fig. 4. node B1 is an ancestor for nodes CI, C2, and nodes C1, C2 are descendants of node B1);
level- node layer number counted from the root.
Figure 4. Hierarchical data model
Quantity trees in the database is determined by the number root records. There is only one path to each node from the root.
network structure has the same components as the hierarchical one, but each node can be connected to any other node (Fig. 5). The network approach to data organization is an extension of the hierarchical one. In hierarchical models, a descendant record must have only one parent; in network - the descendant can have any number of ancestors.
Figure 5. Network data model
Both of these models are not widely used due to the complexity of implementing graphs in the form of machine data structures, in addition, it is difficult to carry out information search operations in them.
The most widespread is the third data model - relational, it can also describe a hierarchical and network model. The relational model is focused on organizing data in the form of two-dimensional tables.
Artificial intelligence
The ideas of modeling the human mind have been known since ancient times. For the first time this is mentioned in the work of the philosopher and theologian Raymond Lullia(c.1235 - c.1315) "Great Art", which not only expressed the idea of a logical machine for solving various problems, based on the general classification of concepts (XIV century), but also tried to implement it. Rene Descartes(1596-1650) and Gottfried Wilhelm Leibniz(1646-1716) independently developed the doctrine of the innate ability of the mind to cognize and the universal and necessary truths of logic and mathematics, worked on the creation of a universal language for classifying all knowledge. It is on these ideas that the theoretical foundations of the creation of artificial intelligence are based. Push to further development models of human thinking was the emergence in the 40s. 20th century COMPUTER. In 1948 an American scientist Norbert Wiener(1894-1964) formulated the main provisions of a new science - cybernetics. In 1956, at Stanford University (USA), at a seminar called “Artificial intelligence * (artificial intelligence) dedicated to solving logical problems, a new scientific direction was recognized related to machine modeling of human intellectual functions and called artificial intelligence. Soon this branch was divided into two main areas: neurocybernetics and "black box" cybernetics.
Neurocybernetics turned to the structure of the human brain as the only thinking object and took up its hardware modeling. Physiologists have long identified neurons - connected with each other nerve cells as the basis of the brain. Neurocybernetics deals with the creation of elements similar to neurons, and their combination into functioning systems, these systems are called neural networks. In the mid 80s. In the 20th century, the first neurocomputer was created in Japan, simulating the structure of the human brain. Its main area of application is pattern recognition.
Black box cybernetics uses other principles, the structure of the model is not the main thing, its reaction to the given input data is important, at the output the model should react like a human brain. Scientists in this area are developing algorithms for solving intellectual problems for existing computing systems. The most significant results:
Maze search model(late 50s), which considers the state graph of an object and searches for the optimal path from the input data to the resulting ones. In practice, this model has not found wide application.
heuristic programming(early 60s) developed action strategies based on previously known predetermined rules (heuristics). Heuristic - a theoretically unfounded rule that allows you to reduce the number of searches in the search for the optimal path.
Methods of mathematical logic. The method of resolutions, which makes it possible to automatically prove theorems on the basis of certain axioms. In 1973, a logic programming language was created Prologue, allowing to process symbolic information.
Since the mid 70s. the idea of modeling the specific knowledge of specialists-experts is being implemented. The first expert systems appear in the USA. A new technology of artificial intelligence is emerging, based on the representation and use of knowledge. Since the mid 80s. artificial intelligence is being commercialized. Investments in this industry are growing, industrial expert systems are emerging, and interest in self-learning systems is increasing.
Knowledge Bases
When studying intelligent systems, it is necessary to find out what knowledge is and how it differs from data. concept knowledge defined in various ways, but there is no definitive definition.
Here are some of the definitions:
Knowledge- identified patterns of the subject area (principles, connections, laws), allowing to solve problems in this area.
Knowledge well-structured data, or data about data, or metadata.
Knowledge- a set of information that forms a holistic description corresponding to a certain level of awareness about the described issue, object, etc.
From the point of view of artificial intelligence, knowledge is defined as formalized information that is referred to in the process of inference. Knowledge bases are used to store knowledge. Knowledge base- the basis of any intellectual system.
From the point of view of solving problems in a certain subject area, it is convenient to divide knowledge into two categories - data and heuristics. The first category describes the circumstances known in the field, knowledge of this category is sometimes called textual, emphasizing their sufficient description in the literature. The second category of knowledge is based on the practical experience of an expert in this subject area.
In addition, knowledge is divided into procedural and declarative. Historically, procedural knowledge was the first to appear, “scattered” in algorithms. They managed the data. To change them, it was necessary to make changes to the programs. With the development of artificial intelligence, an increasing part of knowledge was formed in data structures: tables, lists, abstract data types, knowledge became more and more declarative.
Declarative knowledge- this is a collection of information about the characteristics of the properties of specific objects, phenomena or processes, presented in the form of facts and heuristics. Historically, such knowledge has been accumulated in the form of various directories; with the advent of computers, it has acquired the form of databases. Declarative knowledge is often referred to simply as data; it is stored in the memory of an information system (IS) in such a way that it is directly accessible for use.
procedural knowledge are stored in the memory of the IC in the form of descriptions of the procedures by which they can be obtained. In the form of procedural knowledge, methods for solving problems in the subject area, various instructions, techniques, etc. are usually described. Procedural knowledge is methods, algorithms, programs for solving various problems in the selected subject area, they form the core of the knowledge base. Procedural knowledge is formed as a result of the implementation of procedures on facts as initial data.
One of the most important problems specific to artificial intelligence systems is knowledge representation. The form of knowledge representation significantly affects the characteristics and properties of the system. To manipulate various knowledge of the real world on a computer, it is necessary to simulate them. There are many knowledge representation models for various subject areas, but most of them belong to the following classes: logical models", production models; semantic networks; frame models.
Traditionally, in knowledge representation, there are formal logic models, based on the classical first-order predicate calculus, when the subject area is described as a set of axioms. All information necessary for solving problems is considered as a set of rules and statements, which are presented as formulas in some logic of predicates. Knowledge reflects the totality of such formulas, and obtaining new knowledge is reduced to the implementation of inference procedures. This logical model is applicable mainly in research "ideal" systems, as it imposes high requirements and limitations of the subject area. Industrial expert systems use its various modifications and extensions.
Studies of human decision-making processes have shown that when reasoning and making a decision, a person uses production rules(from English. production is the rule of inference that generates the rule). production model, based on the rules, allows you to present knowledge in the form of sentences: IF (a list of conditions), THEN (a list of actions should be performed). Condition - is the sentence that is searched for in the knowledge base, and action there is some operation performed on a successful search. Actions can be intermediate, acting further as conditions, and targeted completing the work of the IS. In the production model, the knowledge base consists of a set of rules. The program that manages the enumeration of rules is called output machine. The mechanism of inference links knowledge and creates a conclusion from their sequence. Conclusion happens straight(matching method, from data to target search) or back(a method of generating a hypothesis and testing it, from the goal to the data).
Example. There is a fragment of the knowledge base, consisting of two rules:
Etc. 1: IF "doing business" and "getting to know the Internet",
TO "electronic commerce".
Etc. 2: IF "owns a computer"
TO "acquaintance with the Internet".
Data entered into the system: "Doing Business" and "owns a computer."
DIRECT OUTPUT: Get a conclusion based on the available data.
1st pass:
Step 1. Checking Ex. 1, does not work - there is not enough "familiarity with the Internet" data.
Step 2. Check Ex. 2, works, the base is supplemented by the fact "acquaintance with the Internet".
2nd pass
Step 3. Checking Ex. 1, works, the system gives the conclusion "electronic commerce".
REVERSE CONCLUSION: Confirm the selected target using the available rules and data.
1st pass:
Step 1. Goal - "e-commerce":
We check Pr. 1, there is no "familiarity with the Internet" data, they become a new goal, and there is a rule where it is on the right side.
Step 2. Goal - "acquaintance with the Internet":
Etc. 2 confirms the target and activates it.
2nd pass: Step 3. Ex. 1 confirms the desired target.
The production model attracts developers with its visibility, modularity, ease of additions and changes, simplicity of the inference mechanism, most often used in industrial expert systems.
Semantics is a science that studies the properties of signs and sign systems, their semantic connection with real objects. Semantic web - this is a directed graph whose vertices are concepts, and the arcs are the relationships between them (Fig. 6). This is the most general model of knowledge, since it contains the means of all properties characteristic of knowledge: internal interpretation, structuredness, semantic metrics and activity.
Figure 6. Semantic Web
The advantages of network models are: great expressive possibilities; visibility of the knowledge system presented graphically; the proximity of the network structure representing the knowledge system to the semantic structure of phrases in natural language; compliance with modern ideas about the organization of long-term human memory. The disadvantages include the fact that the network model does not contain a clear idea of the structure of the subject area that corresponds to it, so its formation and modification are difficult; network models are passive structures; a special apparatus is used for their processing formal conclusion. The problem of finding a solution in a knowledge base such as a semantic network is reduced to the task of finding a network fragment corresponding to a certain subnet of the task, which, in turn, indicates another drawback of the model - the difficulty of finding an inference on semantic networks.
Network models are a visual and fairly universal means of knowledge representation. However, their formalization in specific models representation, use and modification of knowledge is a rather laborious process, especially in the presence of multiple relationships between concepts.
Term frame(from the English frame - frame, frame) is proposed to designate the structure of a unit of knowledge, which can be described by a certain set of concepts, for its spatial perception. The frame has a certain internal structure, consisting of a set of elements called slots. Each slot, in turn, is represented by a certain data structure, procedure, or may be associated with another frame. The frame model is a technological model of human memory and consciousness systematized in the form of a single theory. Unlike other models, a rigid structure is fixed in frames. In general, a frame is defined as follows:
(FRAME NAME: (1st slot name: 1st slot value);
(2nd slot name: 2nd slot value);
(N-ro slot name: N-ro slot value)).
An important property of frames is property inheritance, borrowed from the theory of semantic networks. Inheritance occurs through AKO-links (from A Kind Of, which means "this".). The ACO slot points to a frame of a higher level of the hierarchy, from where it is implicitly inherited, i.e. values of similar slots are transferred. For example, in the network of frames in Fig. 7 "constructor" inherits the properties of the "engineer" and "person" frames, which are at a higher level of the hierarchy.
Figure 7. Network of frames
The frame model is quite universal, it allows you to display all the diversity of knowledge about the world through:
frames-structures, to designate objects and concepts (lecture, abstract, department);
role frames(student, teacher, dean);
script frames(taking an exam, celebrating a name day, receiving a scholarship);
situation frames(anxiety, work mode of the school day), etc. The main advantage of frames as a model of knowledge representation is their ability to reflect the conceptual basis of the organization of human memory, as well as flexibility and visibility.
Summarizing the analysis of knowledge representation models, we can draw the following conclusions:
The most powerful are mixed knowledge representation models.
Expert systems
Designed to analyze data contained in knowledge bases and issue recommendations at the user's request. They are used in cases where the initial data are well formalized, but special extensive knowledge is required to make a decision. Expert systems- these are complex software systems that accumulate knowledge of specialists in specific subject areas and replicate this empirical experience for consultations of less qualified users.
Subject areas: medicine, pharmacology, chemistry, geology, economics, law, etc., in which most of the knowledge is personal experience high-level specialists (experts) need expert systems. Those areas where most of the knowledge is presented in the form of collective experience (for example, higher mathematics) do not need them.
The expert system is defined by a set of logically interconnected rules that form the knowledge and experience of a specialist in this subject area, and a decision mechanism that allows you to recognize the situation, give recommendations for action, and make a diagnosis.
Modern expert systems are capable of:
Based on the totality of the signs of the disease, establish a diagnosis, prescribe treatment, dose medications, develop a program for the course of treatment;
Perform the tasks of diagnostic systems in the study of phenomena and processes (for example, for blood analysis; production management; studying the state of the earth's interior, oil fields, coal deposits, etc.);
Recognize speech, at this stage in a limited scope;
Recognize human faces, fingerprints, etc.
On fig. 8 shows the main components of the expert system model: user(domain specialist for whom this system is intended), knowledge engineer(an artificial intelligence specialist is an intermediate link between an expert and a knowledge base), user interface(an application that implements a dialogue between the user and the system), knowledge base - expert system core, solver(an application that simulates the reasoning of an expert based on the knowledge in the database), clarification subsystem ( an application that allows you to explain on the basis of which the expert system makes recommendations, draws conclusions, what knowledge is used in this case ), intelligent knowledge base editor(an application that gives the knowledge engineer the ability to create a knowledge base online ).
Figure 8. The structure of the expert system model.
A characteristic feature of any expert system is the ability to self-develop. The initial data is stored in the knowledge base in the form of facts between which certain logical relationships are established. If the testing revealed incorrect recommendations or conclusions on specific issues, or the conclusion cannot be formulated, this means that either important facts in its base, or violations in the logical system of connections. In any case, the system itself can generate a sufficient set of questions for the expert and automatically improve its quality.
Control system
Represents a set of interrelated structural models of subsystems that carry out following features:
planning(strategic, tactical, operational);
accounting- displays the state of the control object as a result of execution production processes;
control- determines the deviation of accounting data from the planned goals and standards;
operational management- regulates all processes in order to eliminate emerging deviations from planned and accounting data;
analysis- determines the trend in the operation of the system and the reserves that are taken into account when planning for the next time period.
The use of models as part of information systems began with the use of statistical methods and methods financial analysis, which were implemented by commands of conventional algorithmic languages. Later, special languages \u200b\u200bwere created to simulate various situations. Such languages make it possible to build models of a certain type that provide a solution for a flexible change in variables.
SOFTWARE. BASIC PROGRAMMING CONCEPTS
BASIC CONCEPTS AND DEFINITIONS
The considered technical means of a PC together are a universal tool for solving a wide range of problems. However, these problems can be solved only if the PC "knows" the algorithm for solving them.
Algorithm(algorithm) - a precise prescription that defines the process of transforming the initial data into the final result.
General properties of any algorithm are:
– discreteness – the possibility of splitting the algorithm into separate elementary actions;
– certainty (determinism) of the algorithm ensures the unambiguity of the result (repeatability of the result obtained in multiple calculations with the same initial data) and eliminates the possibility of distortion or ambiguous interpretation of the prescription;
– effectiveness – obligatory receipt of a certain result in a finite number of steps, and if it is impossible to obtain a result, a signal that this algorithm is not applicable to solve the problem;
– mass character – the possibility of obtaining a result with different initial data for a certain class of similar problems.
Classical approach to building models- the approach to the study of the relationship between the individual parts of the model provides for their consideration as a reflection of the relationship between the individual subsystems of the object. This (classical) approach can be used to create fairly simple models.
Thus, the development of a model M based on the classical approach means the summation of individual components into a single model, with each of the components solving its own problems and being isolated from other parts of the model. Therefore, the classical approach can be used to implement relatively simple models in which separation and mutually independent consideration of individual aspects of the functioning of a real object are possible.
Two distinctive aspects of the classical approach can be noted:
There is a movement from the particular to the general,
The created model is formed by summing its individual components and does not take into account the emergence of a new systemic effect.
Systems approach- this is an element of the doctrine of the general laws of the development of nature and one of the expressions of the dialectical doctrine.
With a systematic approach to modeling systems, it is necessary first of all to clearly define the purpose of modeling. Since it is impossible to fully model a really functioning system, a model (system-model, or second system) is created for the problem posed. Thus, in relation to modeling issues, the goal arises from the required modeling tasks, which allows you to approach the choice of criterion and evaluate which elements will be included in the created model M. Therefore, it is necessary to have a criterion for selecting individual elements in the created model.
Important for the system approach is the definition of the structure of the system - the totality of links between the elements of the system, reflecting their interaction.
A systematic approach allows solving the problem of building a complex system, taking into account all factors and opportunities proportional to their significance, at all stages of studying the system S and building a model M.
The systems approach means that each system S is an integrated whole even when it consists of separate disparate subsystems. Thus, the system approach is based on the consideration of the system as an integrated whole, and this consideration during development begins with the main thing - the formulation of the goal of functioning.
With a structural approach the composition of the selected elements of the system S and the connections between them are revealed. The totality of elements and links between them makes it possible to judge the structure of the system. The latter, depending on the purpose of the study, can be described at different levels of consideration. The most general description of the structure is a topological description, which makes it possible to define the constituent parts of the system in the most general terms and is well formalized on the basis of graph theory.
With a functional approach individual functions are considered, i.e., algorithms of the system's behavior, and a functional approach is implemented that evaluates the functions that the system performs, and the function is understood as a property that leads to the achievement of the goal. Since the function displays the property, and the property displays the interaction of the system S with the external environment E, the properties can be expressed as either some characteristics of the elements Si(j) and subsystems Si, the system, or the system S as a whole.
The main stages of evaluation of complex systems.
Stage 1. Determining the purpose of the assessment. There are two types of goals in systems analysis. A goal is called qualitative, the achievement of which is expressed in a nominal scale or in a scale of order. A quantitative goal is called, the achievement of which is expressed in quantitative scales.
Stage 2. Measurement of the properties of the system that are considered significant for the purposes of the evaluation. To do this, appropriate scales are selected for measuring properties, and all the studied properties of systems are assigned a certain value on these scales.
Stage 3. Substantiation of preferences for quality criteria and criteria for the efficiency of systems functioning based on the properties measured on the selected scales.
Stage 4. The actual assessment. All the systems under study, considered as alternatives, are compared according to the formulated criteria and, depending on the objectives of the assessment, are ranked, selected, and optimized.
The following typical groups of models can be used as the basis for the classification system of mathematical models:
– static and dynamic;
– deterministic and stochastic;
– discrete and continuous.
Each specific system S is characterized by a set of properties, which are understood as quantities that reflect the behavior of the simulated object (real system) and take into account the conditions for its functioning in interaction with the external environment (system) E.
The initial information in the construction of MM processes of systems functioning is data on the purpose and operating conditions of the studied (designed) system S. This information determines the main purpose of modeling, requirements for MM, the level of abstraction, and the choice of a mathematical modeling scheme.
mathematical scheme can be defined as a link in the transition from a meaningful to a formalized description of the system functioning process, taking into account the impact of the external environment. Those. there is a chain: descriptive model - mathematical scheme - simulation model.
concept mathematical scheme allows us to consider mathematics not as a method of calculation, but as a method of thinking, a means of formulating concepts, which is most important in the transition from a verbal description to a formalized representation of the process of its functioning in the form of some MM.
When using a mathematical scheme, first of all, the researcher of the system should decide on the adequacy of the display in the form of specific schemes of real processes in the system under study, and not the possibility of obtaining an answer (solution result) to a specific research question.
For example, the representation of the process of functioning of the IVS for collective use in the form of a network of queuing schemes makes it possible to describe well the processes occurring in the system, but with complex laws of incoming flows and service flows, it does not make it possible to obtain results in an explicit form.
When constructing an MM system S, it is necessary to resolve the issue of its completeness. The completeness of modeling is regulated mainly by the choice of boundaries "System S - environment E". The task of simplifying the MM should also be solved, which helps to highlight the main properties of the system, discarding the secondary goals in terms of modeling.
MM of the simulation object, i.e. systems S can be represented as a set of quantities describing the process of functioning of a real system and forming in the general case the following subsets:
Set X - input actions on S x i ОХ, i=1…n x ;
The totality of environmental influences v lОV, l=1…n v ;
The set of internal (intrinsic) parameters of the system h k ОH, k=1…n h ;
The set of output characteristics of the system y j ОY, j=1…n y .
In the enumerated sets, it is possible to distinguish controlled and uncontrolled quantities. In the general case, X, V, H, Y are disjoint sets that contain both deterministic and stochastic components. Input actions E and internal parameters S are independent (exogenous) variables, Output characteristics - dependent variables (endogenous). The process of functioning S is described by the operator F S:
exit trajectory. F S - the law of functioning of S. F S can be a function, functional, logical conditions, algorithm, table or verbal description of the rules.
Functioning algorithm A S - method for obtaining output characteristics, taking into account input effects Obviously, the same F S can be implemented in different ways, i.e. with the help of many different A S .
Relation (2.1) is a mathematical description of the behavior of the simulation object S in time t, i.e. reflects it dynamic properties, such models are called dynamic models. (2.1) is a dynamic model of the system S. For static MMs, it is a mapping of the sets (X, V, H) to (Y), i.e.
Relations (2.1), (2.2) can be given by formulas, tables, etc.
Such ratios in a number of cases can be obtained through the properties of the system at specific points in time, called states. The states of the system S are characterized by vectors:
AND , where at the moment t l н(t 0 , T)
At the moment t ll О(t 0 , T), etc. k=1…n Z .
Z 1 (t), Z 2 (t)… Z k (t) are the coordinates of a point in k-dimensional phase space. Each implementation of the process will correspond to some phase trajectory.
The set of all possible values of states ( ) is called the state space of the modeling object Z, and z k нZ.
The state of the system S in the time interval t 0 otherwise: . (2.5) Time in the model S can be considered on the simulation interval (t 0 , T) as continuous or discrete, i.e. quantized on a segment of lengths. Dt. Thus, the MM of an object is understood as a set of finite sets of variables ( ) together with mathematical relationships between them and output characteristics . If the operators F, Ф, impacts X, V, and characteristics h do not contain elements of randomness, then the model is called deterministic in the sense that the characteristics are uniquely determined by deterministic input actions: As deterministic models, when a random fact is not taken into account in the study, differential, integral, and other equations are used to represent systems operating in continuous time, and to represent systems operating in discrete time - finite automata and finite difference schemes. Deterministic modeling is a special case of stochastic modeling. As stochastic models (taking into account the random factor), probabilistic automata are used to represent systems with discrete time, and queuing systems (QS) are used to represent systems with continuous time. Of great practical importance in the study of complex individual management systems, which include automated control systems, are the so-called aggregative models. Aggregative models (systems) make it possible to describe a wide range of research objects with a display of the systemic nature of these objects. It is during the aggregative description that a complex object is divided into a finite number of parts (subsystems), while maintaining connections, ensuring the interaction of parts. Thus, when constructing mathematical models of the processes of functioning of systems, the following main approaches can be distinguished: continuous-deterministic (for example, differential equations); discrete-deterministic (finite automata); discrete stochastic (probabilistic automata); continuous-stochastic (queuing systems); generalized, or universal (aggregative systems). These approaches are used in the construction of mathematical schemes. Typical mathematical schemes are :
differential equations, finite and probabilistic automata, queuing systems, Petri nets, etc. Typical mathematical schemes have the advantage of simplicity and clarity, but with a significant narrowing of the possibility of application. To obtain mathematical models, two ways are used: theoretical and experimental. Accordingly, they distinguish theoretical and empirical models. According to the degree of accounting for time and acting forces, mathematical models are divided into static, kinetic, dynamic. Static models determine the final, critical, equilibrium values of the parameters of the process, system. These include models of the state of the material, the relationship of input x and output y variables. Static models are widely used in mineral processing to determine the energy and material balances of various apparatuses and processes, including those under design. Unlike static, kinetic and dynamic models contain time as an argument. Kinetic models or characterize the course of the process in time and relate its parameters to time. They are obtained by integrating differential equations under certain initial conditions. Dynamic models describe the patterns of changes in the state of bodies, masses under the influence of forces applied to them F in various environments. The basis for describing dynamic models is differential equations, which describe the vast majority of automatic control systems. Such models describe transient modes in systems. Requirements for the mathematical model: 1. The mathematical model must be suitable for solving the problem. 2. Must take into account physical and mathematical limitations. 3. Must reproduce the process with the accuracy necessary for the researcher, i.e. to be adequate process. Instructions for compiling a mathematical model: 1. Decompose the general task of studying the system into a number of simpler tasks. 2. Clearly formulate goals. 3. Find analogues. 4. Consider a numerical example. 5. Select certain designations. 6. Write down the obvious relationships. 7. If the resulting model lends itself to a mathematical description, expand it, otherwise simplify.
