System approach in system modeling. System approach to modeling methods and approaches to modeling distributed systems

07.09.2020

Classic approach when building models- The approach to studying the relationship between individual parts of the model provides for consideration of them as a reflection of links between individual object subsystems. Such (classic) approach can be used when creating sufficiently simple models.

Thus, the development of a model M on the basis of a classical approach means summing the individual components into a single model, each of the components solves its own tasks and is 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 the real object.

You can note two distinctive aspects of the classical approach:

There is a movement from private to general,

The created model is formed by summing the individual components and does not take into account the occurrence of a new system effect.

Systems approach - This is an element of teaching on the general laws of nature development and one of the expressions of dialectical teaching.

With a systematic approach to modeling systems, it is necessary to clearly determine the purpose of modeling. Since it is impossible to fully simulate a real-functioning system, a model (model system, or the second system) is created under the problem. Thus, in relation to modeling issues, the target arises from the required modeling tasks, which makes it possible to approach the selection of the criterion and evaluate which items will be included in the model M. Therefore, it is necessary to have a criterion for selecting individual elements to the created model.

An important for the systemic approach is to determine the structure of the system - a set of connections between the elements of the system, reflecting their interaction.

The systematic approach allows to solve the problem of building a complex system, taking into account all the factors and capabilities proportional to their significance, at all stages of the study of the system S and the construction of the model M.

With a structural approachthe composition of the selected elements S system and the relationship between them is detected. The combination of elements and connections between them allows to judge the structure of the system. The latter depending on the purpose of the study can be described at different levels of consideration. Most general description Structures are a topological description, which allows you to determine the composite parts of the system in the most common concepts and is well-formalized on the basis of the theory of graphs.

With a functional approachseparate functions are considered, i.e. the algorithms of the behavior of the system, and the functional approach that estimates the functions that performs the system is being implemented, and the function is understood as the property leading to the achievement of the target. 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 in the form of either some characteristics of elements Si (J) and Si subsystems, - systems or systems S in general.

The main assessment stages of complex systems.

Stage1. Determination of the estimation goal. In system analysis, two types of targets are distinguished. Qualitatively called the goal, the achievement of which is expressed in the nominal scale or in the scale of order. Quantitative called the goal, the achievement of which is expressed in quantitative scales.

Stage2. Measuring the properties of a system recognized by substantially for estimation purposes. For this, the corresponding scales for measuring properties and all the test properties under study are assigned a certain value on these scales.

Stage3. Justification of the preferences of quality criteria and criteria for the efficiency of systems based on the properties measured on selected scales.

Stage4. Actually evaluation. All the underlying systems considered as alternatives are compared according to the criteria and, depending on the purposes of estimation, are ranked, selected, are optimized.

Concept of system

We live in a world that consists of many different objects having a variety of properties and interacting with each other. For example, the objects of the world are planets Solar systemwhich have different properties (mass, geometric dimensions, etc.) and interact with the Sun and among themselves according to the law of global gravity.

Each planet is part of a larger object - the solar system, which in turn is part of the galaxy. At the same time, each planet consists of atoms of different chemical elements, which consist of elementary particles. Thus, in fact, each object may consist of a set of other objects, i.e. Forms the system.

An important sign of the system is its holistic functioning. The system is not a set of individual elements, but a set of interrelated elements. For example, a personal computer is a system that consists of various devices that are connected with each other and hardware (connected physically to each other) and functionally (exchange information).

Definition 1.

The system is a set of interrelated objects that are called the elements of the system.

Note 1.

Each system has its own structure, which characterizes the composition and properties of elements, their relationship and connection between themselves. The system is able to maintain its integrity under the influence of various external factors and internal changes until its structure is unchanged. In the event of a change in the structure of the system (for example, when one of its elements is removed), it may stop working as a whole. For example, when deleting one of the computer devices (for example, the motherboard), the computer will stop working, i.e. it will stop working as a system.

The main provisions of the system theory appeared in the study of dynamic systems and their functional elements. Under the system means a group of interrelated elements that act together in order to fulfill the task in advance. With the help of analyzing systems, you can determine the most real ways to perform the task, which ensure maximum satisfaction of the requirements.

The elements that form the basis of the theory of systems are created not by the help of hypotheses, and they are obtained experimentally. To start building a system, you need to have the general characteristics of technological processes that are necessary and when creating mathematically formulated criteria, which should satisfy the process or its theoretical description. The modeling method is one of the most important methods of scientific research and experimentation.

Systems approach

To build object models, a systematic approach is used, which is a methodology for solving complex tasks. This methodology is based on the consideration of the object as a system that functions in a certain environment. The system approach allows to reveal the integrity of the object, to identify and explore its internal structure, as well as connections with the external environment. At the same time, the object is part of the real world, which is allocated and is investigated due to the solid task of constructing the model. In addition, when using a systemic approach, a sequential transition from common to particular, which is based on the consideration of the design goal, and the object is considered in relation to the environment.

A complex object can be separated on subsystems that represent parts of the object and meet such requirements:

the subsystem is a functionally independent part of the object, which is associated with other subsystems and exchanges information and energy with them;
each subsystem may have functions or properties that do not coincide with the properties of the entire system;
each subsystem can share to the level of elements.

Under the element here is understood as the lower-level subsystem, which is not further divided by appropriate from the position of the problem being solved.

Note 2.

Thus, the system is represented as an object consisting of a set of subsystems, elements and links for its creation, research or improvement. At the same time, the consolidation of the presentation of the system, which includes the main subsystems and relations between them, is called a macrostructure, and a detailed consideration of the internal structure of the system to the level of elements - microstructure.

The concept of the overseystem is usually associated with the concept of the system - more high levelThe composition of which includes the object in question, and the function of any system can only be determined through the oversystem. It is also important that the concept of the environment is a set of outside world objects, which significantly affect the efficiency of the system functioning, but are not part of the system and its overseystem.

In the system approach to building models, the concept of infrastructure is used, which describes the relationship of the system with its environment (medium).

Selection, description and study of the properties of an object, which are essential for a specific task, is called the stratification of the object.

With a systematic approach in modeling, it is important to determine the structure of the system, which is defined as a set of bonds between the elements of the system, which reflect their interaction.

There are a structural and functional approach to modeling.

With a structural approach, the composition of the isolated elements of the system and the relationship between them is determined. The combination of elements and links is the structure of the system. Usually, a topological description is used to describe the structure, which allows the components of the system and determine their links using graphs.

Less frequently applies a functional description, in which individual functions are considered - the system behavior algorithms. This implements a functional approach that determines the functions that run by the system.

With a systematic approach, various sequences of development of models based on two main design stages are possible: macroprojecting and micropros. At the macroproject stage, the model of the external environment is built, resources and limitations are detected, the system of the system and the criteria are chosen to evaluate adequacy.

The microprojecting stage depends on the type of the selected model. This stage involves the creation of information, mathematical, technical or software Simulation systems. When microprosing, the main specifications The created model, estimate the time of working with it and the costs of resources to obtain the desired quality of the model.

When building a model, regardless of its type, it is necessary to adhere to the principles of a systematic approach:

consistently move across the stages of creating a model;
coordinate information, resource, reliable and other characteristics;
properly relate different levels of constructing the model;
adhere to the integrity of individual stages of model design.

Static information models

Any system continues its existence in space and in time. At different points in time, the system is determined by its state, which describes the composition of the elements, the values \u200b\u200bof their properties, the magnitude and nature of the interaction between the elements, etc.

For example, the state of the solar system at certain points in time is described by the composition of the objects that are included in it (sun, planets, etc.), their properties (size, position in space, etc.), the size and nature of their interaction (the strength of gravity, electromagnetic waves and etc.).

Models that describe the system status at a certain point in time are called static information models.

For example, in physics, static information models are models that describe simple mechanisms, in biology - models of the structure of plants and animals, in chemistry - models of the structure of molecules and crystalline lattices, etc.

Dynamic information models

The system may vary over time, i.e. There is a process of changing the system. For example, when moving planets, their position is changed relative to the Sun and among themselves; Changes chemical composition Sun, radiation, etc.

Models that describe the processes of changes and development of systems are called dynamic information models.

For example, in physics, dynamic information models describe the movement of bodies, in chemistry - the processes of passage of chemical reactions, in biology - the development of organisms or animal species, etc.

Classic approach - Studying relationships between individual parts, and the development of the system model is considered as the summation of individual components into the general model. It is advisable to implement relatively simple models with the separation of individual functions of the real object and deciding on the independence of these functions.

The synthesis process of the model M based on a classic (inductive) approach is shown in Fig. 1.1, a. The real object to be modeling is divided into separate subsystems, i.e., the initial data is selected for modeling and are set to C, displaying individual sides of the simulation process. According to a separate set of source data, the purpose of modeling a separate side of the functioning of the system is set, some component for the future model is formed on the basis of this purpose. The combination of the component is combined into the model M. Thus, the development of the model M on the basis of a classical approach means the summation of individual components into a single model, each of the components solves its own tasks and is isolated from other parts of the model.

Systems approach - This is an element of teaching on the general laws of nature development and one of the expressions of dialectical teaching. You can bring different definitions of the system approach, but most correctly, which allows you to estimate the informative essence of this approach with this method of researching systems as modeling. Therefore, the allocation of the System S system itself and the external environment is very important from objectively existing reality and the description of the system based on the system-wide positions.

A systematic approach means that each System S is integrated as integrated even when it consists of separate disassembled subsystems. Thus, the systemic approach is based on the consideration of the system as an integrated whole, and this consideration of the development begins with the main one - the formulation of the functioning goal. The synthesis process of the model M based on the system approach is conditionally presented in Fig. 1.1, b. Based on the source data, D, which are known from the analysis of the external system, the restrictions that are superimposed on the system from above or on the basis of its implementation capabilities, and based on the purpose of functioning, the source requirements of the system are formulated. On the basis of these requirements, approximately some subsystems P, elements E and the most difficult stage of synthesis is carried out - a choice in the components of the system, for which special criteria for choosing a sq.

Topic 5. Model approach

Model is an abstract description of the system (object, process, problems, concepts) in some form other than the form of their real existence

Modeling begins with the formation of research subjects - concepts of concepts reflecting significant to simulate the characteristics of the object. This task is quite complicated, which is confirmed by various interpretations in the scientific and technical literature of such fundamental concepts as a system, model, modeling. Such ambiguity does not indicate the erroneousness of some and the correctness of other terms, but reflects the dependence of the subject of research (modeling) both from the object under consideration and the objectives of the researcher. A distinctive feature of modeling complex systems is its multifunctionality and manifold of use methods; It becomes an integral part of everything life cycle Systems. This is primarily due to the technologicality of models implemented on the basis of computing equipment: a sufficiently high speed to obtain modeling results and their relatively low cost.

Approaches to system modeling

Currently, when analyzing and synthesizing complex (large) systems, a systematic approach has been developed, which differs from the classic (or inductive) approach. The latter considers the system by moving from private to the general and synthesize (designed) the system by fusion of its component developed separately. In contrast, the systemic approach involves a sequential transition from a common one to the private, when the consideration is based on the purpose, and the object under study is distinguished from the environment.

With a systematic approach to modeling systems, it is necessary, first of all, clearly define the purpose of modeling. Since it is impossible to fully simulate a real-functioning system (the original system, or the first system), a model (system-model system, or the second system) is created for the delivered problem. Thus, in relation to modeling issues, the goal occurs from the required modeling tasks, which allows you to approach the selection of the criterion and evaluate which items will enter the model being created M.. Therefore, it is necessary to have a criterion for the selection of individual elements in the created model.

An important for the systemic approach is to determine the structure of the system - a set of connections between the elements of the system, reflecting their interaction. The structure of the system can be studied from outside from the point of view of the composition of individual subsystems and relations between them, as well as from the inside, when individual properties are analyzed, allowing the system to achieve a given target, i.e. when the system functions are studied. In accordance with this, there was a number of approaches to the study of the structure of the system with its properties, to which, first of all, should include structural and functional.

With a structural approach, the composition of the isolated elements of the S system and the relationship between them is detected. The combination of elements and connections between them allows 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 determine the composite parts of the system in the most common concepts and is well formalized on the basis of the theory of graphs.

Less common is a functional description when individual functions are considered, i.e. The algorithms of the system behavior, and the functional approach is implemented, the evaluating functions that the system performs, and the function is understood as the property leading to the achievement of the target. Since the function displays the property, and the property displays the system interaction S. With an external environment W.then properties can be expressed in the form of either some characteristics of the elements s I. and subsystems S j.or systems S. generally.

If there is some reference standard, you can enter quantitative and qualitative characteristics of systems. For quantitative characteristics, the numbers expressing relations between this characteristic and the standard are introduced. Qualitative characteristics of the system are, for example, using the method of expert assessments.

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 C.. When operating the system S. The quality of its functioning is very important, determined by the effectiveness indicator and which is the value of the effectiveness evaluation criterion. There are various approaches to the choice of evaluation criteria. System S. It can be estimated either a set of private criteria, or some common integral criterion.

It should be noted that the created model M. From the point of view of the system approach is also a system, i.e. S."= S." (M.), and can be considered in relation to the external environment W.. The most simple on the presentation of the model in which the direct analogy of the phenomenon is preserved. There are also models in which there is no direct analogy, but only laws and general patterns of behavior of the elements of the system are stored S.. Proper understanding of relationships as inside the model itself M.and the interaction of it with the external environment W. It is largely determined by what level is the observer.

The synthesis process of the model M. Based on the system approach in Fig.5.1.

When modeling, it is necessary to ensure maximum efficiency of the system model. Efficiency is usually defined as some difference between any indicators of the value of the results obtained in the end of the operation of the model, and the costs that were invested in its development and creation.

Regardless of the type of model used M. When it is built, it is necessary to be guided by a number of principles of a systematic approach: 1) proportional and consistent promotion in stages and directions for creating a model; 2) coordination of information, resource, reliable and other characteristics; 3) the correct ratio of individual levels of hierarchy in the modeling system; 4) the integrity of individual separable stages of the model construction.

Model M. Must answer the specified purpose of its creation, therefore, individual parts should be interconnected, based on a single system task. The goal can be formulated qualitatively, then it will have greater meaningful and for a long time can display the objective possibilities of this modeling system. With a quantitative formulation, the target function arises, which accurately displays the most significant factors affecting the achievement of the target.

The construction of the model refers to the number of systemic tasks, when solving which synthesize solutions on the basis of a huge number of source data, based on the proposals of large teams of specialists. Using a systematic approach in these conditions allows not only to build a model of a real object, but also on the basis of this model to select the required amount of control information in the real system, evaluate the indicators of its operation and thus on the basis of modeling to find the most efficient design and profitable mode of operation of the real system S..

Classical(or inductive) approachthe modeling is considering the system, moving from private to a common, and synthesizes it by fusion component developed separately. Systems approachit implies a consistent transition from the total to particular when the consideration is based on the purpose, while the object is allocated from the world.

When creating a new object with useful properties (for example, control systems) are set criteriadetermining the degree of utility properties. Since any modeling object is a system of interconnected elements, we introduce the concept of the system. System S.there are targeted many interrelated elements of any nature. External environment. E.it is a set of existing outside the system of elements of any nature that influence the system or under its impact.

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 Case time-consuming and expensive, therefore the model is created under a specific purpose.

Important for the system approach is to determine system structures- A combination of links between the elements of the system reflecting their interaction. There are a number of hikes to the study of systems and its properties, which should include structural and functional. For structural approachthe composition of the dedicated elements of the system is detected. S.and the relationship between them. The combination of elements and links allows you to judge the properties of the selected part of the system. For functional approachthe functions (algorithms) of the system behavior are considered, and each function describes the behavior of one property with external influence E.Such an approach does not require knowledge of the system structure, and its description consists of a set of functions of its reaction to external influences.

The classic method of constructing the model uses a functional approach in which the model is accepted as an element componentdescribing the behavior of one property and does not show the real composition of the elements. In addition, the system components are isolated from each other, which poorly reflects the simulated system. This method of constructing the model is applicable only for simple systems, as it requires inclusion in the functions that describe the properties of the system, the relationship between properties that may be poorly defined or unknown.

With the complication of simulated systems when it is impossible to take into account all the mutual influences of the properties applied system method,founded on a structural approach. In this case, the system S.divided into a number of subsystems S L.with its properties that, naturally, it is easier to describe with functional dependencies, and the links between subsystems are determined. In this case, the system operates in accordance with the properties of individual subsystems and links between them. It eliminates the need to describe the functional relationship between system properties S,makes a model more flexible, since the change in the properties of one of the subsystems automatically changes the properties of the system.

Classification of modeling types

Depending on the nature of the processes under study in the system S.and modeling objectives There are many types of models and methods for their classification, for example, by the purpose of using, the presence of random effects, the ratio of the time, the possibility of implementation, the scope of application, etc. (Table 14).

Table 14. Types of models

For usemodels are classified by scientific experimentin which the model is examined using various means of obtaining data on the object, the possibility of influence on the course of the process, in order to obtain new data on the object or phenomenon; complex tests and production experiment,using the field test of the physical object to obtain high reliability of its characteristics; optimizationassociated with finding optimal indicators of the system (for example, finding the minimum costs or the definition of maximum profits).

According to the presence of influencesthe model system is divided into determined(There are no random impacts in systems) and stochastic(In systems there are probabilistic impacts). These same models some authors classify by the method of assessing parameterssystems: B. determinedsystem parameters of the model are estimated by one indicator for specific values \u200b\u200bof their source data; in stochasticsystems The presence of probabilistic characteristics of the source data allows you to evaluate the system parameters with several indicators.

In relation to timemodels divide on staticdescribing the system at a certain point in time, and dynamicconsidering the behavior of the system in time. In turn, dynamic models are divided into discretein which all events occur at time intervals, and continuouswhere all events occur continuously in time.

If possible, salesmodels are classified as mentaldescribing the system that is difficult or impossible to simulate realially realin which the system model is represented by either a real object or part of it, and information,implementing information processes (occurrence, transmission, processing and use of information) on a computer. In turn, mental models are divided into visual(at which the simulated processes and phenomena proceed illustrately); symbolic(The system model represents a logical object in which the main properties and relationships of the real object are expressed by the system of signs or symbols) and mathematical(represent systems of mathematical objects that allow to obtain the studied characteristics of the real object). Real models are divided by saturated(conducting a study on a real facility and the 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).

In terms of applicationmodels divided by universalintended for use by many systems, and specialized,created to study a specific system.

Mathematical models

The most important stage in the construction of the model is the transition from a meaningful description to formal, which is explained by participation at this stage of specialists in the subject area, where there is a modeling system, and specialists in the field of systems modeling. The most convenient language for their communication, the purpose of which is to build an adequate model of the system, usually, is the language of mathematical descriptions. Mathematical description Systems are compact and convenient for further implementations on the computer, in order to conduct statistical tests,

Examples of building dynamic models

When modeling continuous dynamic objects as models, usually perform differential equationsbinding behavior of an object with time. Positive property differential equations It is the fact that the same equation simulates systems of various physical nature.

As an independent variable in dynamic systems, the time from which unknown values \u200b\u200bof the desired function determining the behavior of the object are dependent. Mathematical description of the model in general form:

where - N-dimensional vectors and is continuous.

For example, the process of small oscillations of the pendulum is described by an ordinary differential equation

Process in an electrical oscillatory circuit .

Obviously, if we put

We obtain the equation describing the status in time of both systems

The general mathematical model allows you to explore one system by simulating the operation of another.

Models of dynamic systems based on differential equations were widely used in the theory of management of various technical objects. Under the influence of unknown perturbations, the actual behavior of the system deviates from the desired defined algorithm and to approximate its behavior to the required value, automatic system control is entered into the system. It can be built into the system itself, but when modeling the control unit is separated from the system itself. In the general form, the structure of the multidimensional system of automatic control (SAU) is presented in Fig. 3.

Figure 3. Structure of a multidimensional automatic control system.

Information models

Information modelsin many cases, rely on mathematical models,since when solving the tasks, the mathematical model of the object under study, the process or phenomenon is inevitably converted to the information for its implementation on the computer. We define the basic concepts of the information model.

Information objecta description of a real object, process or phenomenon in the form of a set of its characteristics (information elements), called requisites.The information object of a certain structure (requisite composition) forms type (class),which is assigned unique name.Information object with specific characteristics call instance.Each instance is identified by the task. key props (key).The same details in various information facilities can be both key and descriptive. The information object can have multiple keys.

Example. Information object The student has a detailed composition: number(counting book number - key props), surname, Name, Patronymic, Date of Birth, Training Place Code.Information Object Private: student number, home address, certificate number of secondary education, marital status, children.Information facility Training Place includes details: the code(key props), the name of the university, faculty, group.Information facility teacher: the code(key props), department, surname, first name, patronymic, degree, scientist title, position.

Relations,existing between real objects are determined in information models as communication.There are three types of connections: one to one (1: 1), one to many(1: ∞) and many to many(∞: ∞).

Communication one to onedetermines the correspondence to one instance of the information object X of no more than one instance of the information object Y, and vice versa.

Example. Information facilities The student and private affairs will be associated with the attitude one to one.Each student has certain unique data in the personal case.

When connected one to manyone instance of an information object X can correspond to any number of instances of an information object Y, but each instance of an object Y is associated no more than one instance of the X object.

Example.Between the information objects, the place of study and the student must establish communication one to many.The same learning place can repeatedly repeat for various students.

Communication many to manyensures compliance to one instance of the information object x any number of instances of the object Y, and vice versa.

Example.Information facilities Student and teacher have a connection many to many.Each student is enrolled in many teachers, and every teacher teaches many students.

Examples of information models

We define the information model as a connected set of information objects describing the information processes in the subject area under study. Existing information models are divided into universal and specialized. Universal models are designed for use in various subject areas, they include: databaseand database Management Systems, Automated Management Systems, Knowledge Base, Expert Systems.Specialized models are designed to describe specific systems, are unique in their capabilities, more expensive.

Universal models.

Database

Databasethe associated set of structured data relating to a specific process or phenomenon in a particular subject area is presented.

Database Management Systemit is a software package for creating, organizing the necessary processing, storage and transfer of databases.

Kernel any database is data presentation model.The data model represents a variety of data structures and interrelations between them.

Distinguish hierarchical, networkingand relationaldata model. The hierarchical model represents communication between objects (data) in the form of a tree.

The main concepts of the hierarchical model include:

knot- a set of data attributes describing the object;

Communication- Line connecting low-level nodes with one node of the overlying level. In this case, the node of the overlying level is called ancestralfor the lower level nodes corresponding to it, in turn, the low-level nodes are called descendantsassociated with them overlying node (for example, in Fig. 4. Node B1 - ancestor for Ci nodes, C2, and the nodes C1, C2 - descendants of the node B1);

level- The number of the node layer, counted from the root.

Figure 4. Hierarchical data model

number treesthe database is determined by the number root records.Each node has the only way from the root.

Network structureit has the same components as hierarchical, but each node can be associated with any other node (Fig. 5). The network approach to the organization of data is an expansion of hierarchical. In hierarchical models, the post-descendant should have only one ancestor; In network - a descendant can have any number of ancestors.

Figure 5. Network data model

Both of these models were not widespread due to the complexity of the implementation of graphs in the form of machine data structures, in addition, it is difficult to make information search operations.

The third data model received the scalared - relationalit can also describe the hierarchical and network model. The relational model is focused on the organization of data in the form of two-dimensional tables.

Artificial Intelligence

The ideas of modeling the human mind are known from ancient times. For the first time this is mentioned in the composition of the philosopher and thewoman Ramunda Lully(OK 1235 - OK.1315) "Great Art", which not only expressed the idea of \u200b\u200ba logical machine to solve various tasks, based on the universal classification of concepts (XIV century), but also tried to implement it. Rene Descartes(1596-1650) and Gottfried Wilhelm Leibnitz(1646-1716) independently of each other developed the doctrine of the inborn ability of the mind to the knowledge and universal and necessary truths of logic and mathematics, worked on creating a universal language classification of all knowledge. It is on these ideas based on theoretical basis creating artificial intelligence. The impetus to the further development of the model of human thinking was the appearance of in the 40s. XX century COMPUTER. In 1948, the American scientist Norbert Wiener(1894-1964) formulated the main provisions of the new science - cybernetics. In 1956, in Stenford University (USA) at a seminar called "Artificial Intelligence * (artificial intelligence) on solving logical tasks, a new scientific direction is recognized associated with machine modeling of human intelligent functions and called artificial Intelligence.Soon this industry was divided into two main directions: neurokabernotics and cybernetics of the "black box".

Neurokabernetiche turned to the structure of the human brain as the only to-thinking object and began his hardware modeling. Physiologists have long revealed neurons - nerve cells associated with each other as the basis of the brain. Neurocabernetics is engaged in creating elements similar to neurons, and their association into functioning systems, these systems are called neural networks.In the mid-80s. XX V.V. Japan was created the first neurocomputer, which simulates the structure of the human brain. Its main scope - pattern recognition.

Cybernetics of the Black Boxit uses other principles, the structure of the model is not the main thing, its reaction is important to the specified input data, the model should react as a human brain at the output. Scientists of this direction are engaged in the development of algorithms for solving intellectual tasks for available computing systems. Most significant results:

Model of labyrinth search(The end of the 50s.), in which the object's graph is considered and there is a search for the optimal path from the input data to the result. In practice, this model has not found wide use.

Heuristic programming(The beginning of the 60s) developed strategies for action on the basis of pre-known specified rules (Heuristics). Heuristics -theoretically not reasonable rule that allows you to reduce the number of hopping in the search for the optimal path.

Methods of mathematical logic.The method of resolutions that makes it possible to automatically prove theorems on the basis of certain axioms. In 1973, a language of logical programming was created. Prologue,allowing you to process symbolic information.

From the mid-70s. The idea of \u200b\u200bmodeling specific knowledge of experts is being implemented. The first expert systems appear in the US. Arises new technology Artificial intelligence based on the presentation and use of knowledge. From the mid-80s. Artificial intelligence commercializes. Investments in this industry are growing, industrial expert systems appear, interest in self-learning systems increases.

Knowledge base

When studying intelligent systems, it is necessary to find out what knowledge is also the difference from the data. Concept knowledgedetermine in different ways, but there is no any exhaustive definition.

Here are some of the definitions:

Knowledge - identified patterns of the subject area (principles, communications, laws), allowing to solve problems in this area.

Knowledge - Well-structured data, or data on data, or metadata.

Knowledge - A combination of information forming a holistic description, corresponding to a certain level of awareness of the question described, object, etc.

From the point of view of artificial intelligence, knowledge is defined as formalized information to which referenced in the process of logical output. For knowledge storage use knowledge bases. Knowledge base- The basis of any intellectual system.

From the point of view of solving problems in a certain object of knowledge, it is convenient to divide into two categories - factsand heuristics.The first category describes the circumstances known in this area, the knowledge of this category is sometimes called text, emphasizing their sufficient description in the literature. The second category of knowledge relies on the practical experience of the expert expert of this subject area.

In addition, knowledge is divided by proceduraland declarative.Historically, procedural knowledge, "scattered" in algorithms appeared first. They managed data. To change, it was necessary to make changes to the program. With the development of artificial intelligence, the whole majority of knowledge was formed in data structures: tables, lists, abstract data types, knowledge became increasingly declarative.

Declarative knowledge- This is a combination 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 was accumulated in the form of a variety of reference books, with the appearance of a computer acquired the form of databases. Declarative knowledge is often called simply data, they are stored in the memory of the information system (IP) so that they have direct access to use.

Procedural knowledgestored in IP memory as descriptions of procedures with which they can be obtained. In the form of procedural knowledge, it usually describes the methods for solving the objectives of the subject area, various instructions, techniques, etc. Procedural knowledge is methods, algorithms, programs for solving various tasks in the selected subject area, they constitute the kernel of the knowledge base. Procedural knowledge is formed as a result of the implementation of procedures over the facts as source data.

One of the most important problems characteristic of artificial intelligence systems is the presentation of knowledge. The form of knowledge representation significantly affects the characteristics and properties of the system. For manipulation of various knowledge of the real world on a computer, it is necessary to carry out their simulation. There are many knowledge presentation models for various subject areas, but most of them belong to the following classes: logical models ", product models; semantic networks; frame models.

Traditionally, in the presentation of knowledge allocate formal logical modelsbased on the classical calculation of first-order predicates, when the subject area is described in the form of a set of axioms. All information necessary for solving problems is considered as a set of rules and allegations, which are represented as formulas in some predicate logic. Knowledge reflect a set of such formulas, and obtaining new knowledge is reduced to the implementation of logical output procedures. This logical model is applicable mainly in research "ideal" systems, as it places high demands and limitations of the subject area. In industrial expert systems, its various modifications and expansion are used.

Studies of decision-making processes by a person showed that arguing and making a decision, a person uses production rules(from English. pRODUCTION- The output rule that generates the rule). Production model,founded on the rules, allows you to submit knowledge in the form of proposals: if (condition a list), then (you should perform a list of actions). Condition -this proposal for which the knowledge is found in the knowledge base, and actthere is some operation performed at a successfully implemented search. Actions can be like intermediateprotruding below both the conditions and targetfinishing IP work. In the production model, the knowledge base consists of a set of rules. The program controlling the rules is called with output machine.The conclusion mechanism binds knowledge and creates conclusion from their sequence. The output is straight(method of comparison, from data to the search for goal) or back(The method of generating a hypothesis and its verification, from the target to the data).

Example. There is a fragment of a knowledge base consisting of two rules:

Etc. 1: if "doing business" and "acquaintance with the Internet",

That "e-commerce".

Etc. 2: if "owns a computer",

That "acquaintance with the Internet."

The system received data: "Doing Business" and "Owns computer."

Direct conclusion:Based on the available data to obtain a conclusion.

1st pass:

Step 1. Checking. 1, does not work - there is not enough data "Meet the Internet".

Step 2. Check the PR. 2, it works, the base is complemented by the fact "Acquaintance with the Internet".

2nd pass

Step 3. Checking the PR. 1, works, the system gives an "e-commerce" conclusion.

Return output:Confirm the selected goal through the existing rules and data.

1st pass:

Step 1. Purpose - "E-Commerce":

We check the pr. 1, Data "Acquaintance with the Internet" is not, they become a new goal, and there is a rule where it is in the right part.

Step 2. Purpose - "Acquaintance with the Internet":

Etc. 2 confirms the goal and activates it.

2nd pass: Step 3. PR. 1 confirms the desired goal.

The product model attracts developers with visibility, modularity, ease of adding additions and changes, the simplicity of the logical output mechanism, is most often used in industrial expert systems.

Semantics- This is the science, exploring the properties of signs and iconic systems, their semantic communications with real objects. Semantic network -this is an oriented graph, whose vertices are concepts, and arcs - the relationship between them (Fig. 6). This is the most general model of knowledge, as it has means of all characteristic properties characteristic of knowledge: internal interpretation, structure, semantic metrics and activity.

Figure 6. Semantic network

The advantages of network models are: great expressive opportunities; clarity of the knowledge system presented graphically; The proximity of the network structure representing the knowledge system, the semantic structure of phrases in the natural language; Compliance with modern ideas about the organization of long-term human memory. To disadvantages, we will assume that the network model does not contain a clear idea of \u200b\u200bthe structure of the subject area, which corresponds to it, so its formation and modification are difficult; Network models are passive structures, a special device is used to process them. formal output.The problem of finding a solution in the database of the type of semantic network is reduced to the task of searching for a network fragment corresponding to some subnet of the task, which, in turn, speaks more than one lack of model - the complexity of finding an output on semantic networks.

Network models are visual and enough universal means Presentations of knowledge. However, their formalization in specific models of presentation, use and modification of knowledge represents a rather laborious process, especially in the presence of multiple relations between concepts.

Term fream(From the English. Frame - frame, frame) proposed to designate the structure of a unit of knowledge, which can be described by a certain set of concepts, for its spatial perception. Frame has a certain internal structure consisting of a set of elements called slots.Each slot, in turn, seems to be defined data structure, procedure,or may be associated with another frame. The frame model is systematized in the form of a single theory technological model of human memory and its consciousness. Unlike other models, a rigid structure is fixed in frames. In the general case, the frame is defined as follows:

(Frame name: (1st slot name: 1st slot value);

(The name of the 2nd slot: the value of the 2nd slot);

(N-RO Slot Name: N-RO Slot value)).

An important feature of frames is inheritance properties,borrowed from the theory of semantic networks. Inheritance occurs on AKO-links (from A Kind of, which means "et.e."). The AKO slot indicates a higher level of the hierarchy, from where implicitly inherited, i.e. The values \u200b\u200bof similar slots are transferred. For example, in the framework of frames in Fig. 7 "Designer" inherits the properties of the "Engineer" and "Man" frames, which are at a higher level of the hierarchy.

Figure 7. Frames network

The frame model is quite universal, allows you to display all the diversity of knowledge about the world through:

Frame structures,to designate objects and concepts (lecture, summary, department);

Frame-role(student, teacher, dean);

Scenarios Frames(passing the exam, celebration of the name, receiving scholarships);

Frames situations(Anxiety, working hours of the school day) and others. The main advantage of frames as a knowledge presentation model is their ability to reflect the conceptual basis for organizing human memory, as well as flexibility and visibility.

Summarizing knowledge presentation models, you can draw the following conclusions:

The most powerful are mixed knowledge presentation models.

Expert systems

Designed to analyze the data contained in the knowledge bases and issuing recommendations for the user's request. Used in cases where the initial data is well formalized, but for making a decision requires special extensive knowledge. Expert systems- These are complex software complexes, accumulating knowledge of specialists in specific subject areas and replicating this empirical experience for consulting less skilled users.

Subject areas: medicine, pharmacology, chemistry, geology, economy, jurisprudence, etc., in which most knowledge is personal experiencehigh-level specialists (experts) need expert systems. Those areas where most of the knowledge is represented in the form of collective experience (for example, higher mathematics) do not need them.

The expert system is determined by a set of logically interrelated rules that form knowledge and experience in a specialist of this subject area, and a solution mechanism that makes it possible to recognize the situation, make recommendations for action, diagnose.

Modern expert systems are capable of:

By set of signs of the disease, to diagnose, assign treatment, dispense medicines, to develop a course of treatment;

Perform the tasks of diagnostic systems in the study of phenomena and processes (for example, for blood analysis; production management; study of the state of the bowels of the earth, oil fields, coal deposits, etc.);

Recognize speech at this stage in a limited application;

Recognize human faces, fingerprints, etc.

In fig. 8 depicts the main components of the model of the expert system: user(Specialist of the subject area for which this system designed) engineer for knowledge(specialist in artificial intellect - intermediate link between the expert and the knowledge base), user Interface(application that implements user dialog and system) knowledge base -core of the expert system solver(an application that simulates the argument of an expert based on knowledge available in the database), clarification subsystem (an application that allows you to clarify on the basis of what the expert system gives recommendations, draws conclusions, what knowledge is used ), Intellectual Knowledge Base Editor(An application that gives knowledge engineer the ability to create a knowledge base in dialog ).

Figure 8. Structure of the model of the expert system.

A characteristic feature of any expert system is the ability to self-development. The initial data is stored in the knowledge base in the form of facts between which certain logical connections are installed. If, when testing, incorrect recommendations or opinions on specific issues have been identified, or the conclusion cannot be formulated, this means or lack of important facts in its database, or violations in the logical system of connections. In any case, the system itself can form a sufficient set of questions to the expert and automatically increase its quality.

Control system

Represents a combination of interrelated structural models of subsystems that carry out the following functions:

planning(strategic, tactical, operational);

Accounting- displays the status of the control object as a result of production processes;

control- determines the deviation of credentials from planned goals and standards;

Operational management- regulates all processes in order to eliminate emerging deviations from planned and credentials;

analysis- determines the trend in the operation of the system and reserves, which are taken into account when planning for the next time period.

The use of models in the composition of information systems began with the use of statistical methods and methods of financial analysis, which were implemented by teams of conventional algorithmic languages. Later special languages \u200b\u200bwere created, allowing you to simulate different situations. Such languages \u200b\u200bmake it possible to build models of a certain type that support the solution when a flexible change in variables.

SOFTWARE. Basic programming concepts

Basic concepts and definitions

Considered technical means PEVM combined is a universal tool for solving a wide range of tasks. However, these tasks can be solved only if the PEVM knows the algorithm of their solution.

Algorithm (Algorithm) - an accurate order that defines the process of converting the source data into the end result.

Common properties Any algorithm are:

– discreteness - the possibility of splitting the algorithm into separate elementary actions;

– definition (determinism) The algorithm ensures the definition of the result (the repeatability of the result obtained in multiple calculations with the same source data) and excludes the possibility of distortion or ambiguous interpretation of the prescription;

– performance - mandatory receipt for a finite number of steps of some result, and if it is impossible to obtain the result - the signal that this algorithm is not applicable to solve the task;

– massiness - The possibility of obtaining a result with various source data for some class of similar problems.

Views

Save to classmates Save Vkontakte