2018 № 3(40)

Samigulina G.A., Samigulin T.I. REVIEW OF MODERN APPROACHES OF THE ARTIFICIAL INTELLIGENCE FOR CONTROL SYSTEMS THE COMPLEX OBJECTS
Kucherov A.V., Migov D.A. CALCULATION OF EXPECTED COVERAGE AREA OF WIRELESS SENSOR NETWORK WITH IMPERFECT NODES
Akhatov A.R., Nazarov F.M. DEVELOPMENT OF THE MODEL FOR PREDICTION THE TIME SERIES OF NON-STATIONARY DISCRETE SYSTEMS ON THE BASIC OF NEURON NETWORK
Moiseenko V.V. EXPERIENCE INFORMATIZATION OF THE SOVIET DISTRICT OF NOVOSIBIRSK IN 1970–1990 AND ITS FURTHER USE (RETROSPECTIVE REVIEW)
Narinyani A.S. INTRODUCTION TO SUBDEFINITION

Institute of Information and Computational Technologies, 050010, Republic of Kazakhstan, Almaty
*Satbayev University, 050013, Republic of Kazakhstan, Almaty

REVIEW OF MODERN APPROACHES OF THE ARTIFICIAL INTELLIGENCE FOR CONTROL SYSTEMS THE COMPLEX OBJECTS

UDC 004.89

The article contains an analytical review of the intelligent control systems for the complex objects based on the genetic algorithms, particle swarm optimization and algorithms of ant colony optimization for the period from 2015–2018.

The importance of using bioinspired approaches of artificial intelligence and the prospects for their development are shown. The main advantages and disadvantages of using various intelligent algorithms for the development of the intelligent control systems of the complex objects are given. The relevance of the development of intelligent systems in the creation of the innovative intelligent technologies for various practical applications in industry, oil and gas industry, transport and other areas are shown.

Successful use of the bioinspired algorithms for solving a certain range of the optimization problems showed the promise of using genetic algorithms and algorithms of swarm intelligence.

The analysis of the applied problems presented in the article for the period 2015–2018 and examples sufficiently reveal the scientific significance and prospects of this area of artificial intelligence.

Key words: analytical review, complex object, intellectual control systems, genetic algorithm, particle swarm optimization, ant colony optimization.

References

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15. Jing Wang, Naichao Song, Enyu Jiang, Da Xu, Weihua Deng, Ling Mao. The Application of the Particle Swarm Algorithm to Optimize PID Controller in the Automatic Voltage Regulation System // Proceedings of International Conference on Intelligent Computing for Sustainable Energy and Enviroment. LSMS 2017, ICSEE 2017: Advanced Computational Methods in Energy, Power, Electric Vehicles and Their Integration. Springer Singapore, 2017. P. 529–536.

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18. Fateh Berrouk, Kamel Bounaya. Optimal Power Flow For Multi-FACTS Power System Using Hybrid PSO-PS Algorithms // Journal of Control, Automation and Electrical Systems. Springer, 2018. V. 29, Issue 2. P. 177–191.

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22. Guo-Han Lin, Jing Zhang, Zhao-Hua Liu. Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization // International Journal of Automation and Computing. Springer, 2018. V. 15, Issue 1. P. 103–114.

23. Haiping Ma, Sengang Ye, Dan Simon, Minrui Fei. Conceptual and numerical comparisons of swarm intelligence optimization algorithms // Soft Computing. Springer, 2017. V. 21, Issue 11. P. 3081–3100.

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34. Seyed Mohsen Mousavi, Ardeshir Bahreininejad, S. Nurmaya Musa, Farazila Yusof. A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network // Journal of Intelligent Manufacturing. Springer, 2017. V. 28, Issue 1. P. 191–206.

35. Sidahmed Benabderrahmane. Combining boosting machine learning and swarm intelligence for real time object detection and tracking: towards new meta-heuristics boosting classifiers // International Journal of Intelligent Robotics and Applications. Springer, 2017. V. 1, Issue 4. P. 410–428.

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Bibliographic reference: Samigulina G.A., Samigulin T.I. Review of modern approaches of the artificial intelligence for control systems the complex objects //journal “Problems of informatics”. 2018, № 3. P. 4-20.

article

Kucherov A. V., Migov D. A.*

Novosibirsk State University, 630090, Novosibirsk, Russia
*Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

CALCULATION OF EXPECTED COVERAGE AREA OF WIRELESS SENSOR NETWORK WITH IMPERFECT NODES

UDC 519.17;519.24

Wireless sensor networks (WSN) are widely used for monitoring various objects. It can be a building, a certain area, a perimeter, a human body or an animal’s body, and other objects. The nodes of such a network (sensors) contain transducers that can be very different. The data received from sensor is transferred to the sink via transit sensors using wireless communication. Sink in a WSN is the central functional node that receives and processes all data, or a gateway for data transmission to the base station for processing.

Let’s represent the structure of a WSN in the form of a random un-oriented graph G=(V,E,K), in which: V is a set of vertices, E is a set of edges, K is a set of a dedicated vertices of the graph (terminals) which corresponds to the WSN sinks. The sensors of a WSN are exposed to independent failures, what is described by the probability of the presence of each vertex in the graph (node reliability). In the scope of this paper it is assumed that communication channels are absolutely reliable. And sinks are also absolutely reliable.

In our previous work, we have reviewed such an index of a reliability for WSN with unreliable nodes as a probability of the possibility for network sinks to collect information from a certain number of sensors, which is limited from below by a predetermined threshold value. In this paper, we apply this approach for analysis of a reliable network coverage of some given monitoring area. Two reliability indexes are reviewed: expected value of the coverage area, and the probability that the monitoring area of is not less than the pre-defined threshold value. This area is formed by all points of the plane that are in the neighborhood of a workable sensor connected to the sink via workable nodes.

Algorithms are proposed for the accurate calculation of those indices based on the known factorization method. This technique partitions the probability space into two sets, based on the success or failure of one network’s particular element (node or link). The chosen element is called factored element. So we obtain two subgraphs, in one of them factored element is absolutely reliable (branch of contraction) and in second one factored element is absolutely unreliable that is, absence (branch of removal). The probability of the first event is equal to the reliability of factored element; the probability of the second event is equal to the failure probability of factored element. Thereafter obtained subgraphs are subjected to the same procedure. The law of total probability gives expression for the network reliability.

A strategy of selecting the next element for factorization and various types of final graphs are also proposed and reviewed. In this case, it is necessary to take into account the performance of the connectivity condition of efficient sensors with sink and the availability of their sufficient quantity to cover a given area. In order to ensure connectivity with the sink of the current element during factorization in the course of its reliability increase, it is suggested to select the next element for factorization from adjacent nodes with absolutely reliable nodes which are connected to the sink using the same absolutely reliable sensors. The first element is taken from the adjacent with the sink. With this selection strategy, there is no need for connectivity testing, and all absolutely reliable sensors automatically contribute to the coverage area. If there are no such elements, then we have received a final network.

To calculate the coverage area of final networks several approaches are proposed. Their comparative analysis is given. The pseudocodes of the proposed algorithms and a result of the numerical experiments are given.

Key words: wireless sensor network, network reliability, random graph, factoring method, connectivity, monitoring area.

References

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Bibliographic reference: Kucherov A.V., Migov D.A. Calculation of expected coverage area of wireless sensor network with imperfect nodes //journal “Problems of informatics”. 2018, № 3. P. 21-33.

article

Akhatov A. R., Nazarov F. M.

Samarkand State University, 140104, Uzbekistan, Samarkand

DEVELOPMENT OF THE MODEL FOR PREDICTION THE TIME SERIES OF NON-STATIONARY DISCRETE SYSTEMS ON THE BASIC OF NEURON NETWORK

UDC 621.376.54

Non-stationary processes reflecting technological, economic, social, biological, physical phenomena are characterized by quantitative and qualitative characteristics, which are inherent by intermittent, evolving growth and regulation of parameters over time.

The most suitable mechanism for describing such processes during formalization are dynamic random time series, that allow the use of adaptive modeling methods to develop methods and algorithms for regulating descriptive parameters. An important point here is that in order to achieve model adaptability, it is necessary to use predictive functions that can produce the values of required indicators on the basis of preceding members of time series.

In this study we considered the modeling of non-stationary discrete systems describing processes like payment of educational contracts, electricity consumption, the sale of perishable goods that are carried out in conditions of time constraints and delays in order to build systems for monitoring the payment of educational contracts, forecasting and planning of electricity supply indicators, planning of the volumes of perishable goods purchased for retail trade, in which functions are realized to control the reliability, smoothing, regulation of factors leading to the failure of assigned volumes and deadlines.

Proceeding from theoretical positions and analytical results of known works, the model of non-stationary discrete system under conditions of constraints and delays is proposed. Conditions for the existence and explicit form of scalar control for a non-stationary system are determined.

In view of non-stationarity of investigated processes, and consequently the ambiguity of equations solutions determining the spectrum of transformation matrix of system under conditions of constraints and delays, the task of optimizing control due to analysis, prediction and regulation of perturbing factors parameters is posed.

To control the perturbations of time series with varying characteristics, the method of exponential smoothing is used. This method is one of methods for adaptive prediction and is adapted to quantify the effect that the preceding terms of dynamic series get on the predicted indicator, taking into account their remoteness from the end of considered variables sequence. When using the method of exponential smoothing, the degree of influence of each of dynamic series member on the value of variable is distributed in accordance with the exponential law.

As an estimate of forecast accuracy authors suggested the standard deviation of predicted value by help of which it is possible to calculate the confidence intervals of forecast, since the proposed method of adaptive statistical prediction on the basis of exponential smoothing leaves unresolved the questions of choice of predictive function. The reason for this is that in general case the parabolic model is more efficient than the linear one, however, when variation of the variable time series is stepwise the parabolic model reaches a new level over a longer time interval than the linear one.

According to the proposed method, an algorithm is developed and implemented as a module for approximating and predicting time series. The computational data for three groups of sample data representing stationary periodic (cost of production for example as the production of fruits and vegetables conserves at the enterprises of the Samarkand region, in thousand sums), non-stationary non-periodic (average weighted yield of exchange bonds of the Samarkand region for 2016, in % for year) and non-stationary periodic (the sums of receipts of contract payment to the current account of Samarkand State University, million sum) processes.

To optimize the solution of problems when information is incomplete, noisy, distorted, and when it is practically impossible to obtain acceptable solutions using statistical methods of analytical equalization, a study on the use of models of neural networks was carried out. It is known that neural networks for the processing of data sets provide a significant gain in the speed of the process, as well as in achieving adaptability of the model through training, based on the requirements of the problem.

In this study, we built a neural network model of the economic process using the cascade-forward back propagation neural network architecture, which is simple and efficient in approximating the data while maintaining an acceptable quality of modeling.

The training of a neural network is made on the basis of the most popular and widespread algorithm for back propagation of an error, because all of its characteristics are suitable for the chosen architecture of the network and are convenient for solution of problem posed in the study.

The main purpose of simulation is to build a forecast values of the future state of dynamic series, and to ensure high accuracy of data processing requires the selection of weighting factors that guarantee this accuracy. In this regard, as a parameter of the network, which a priori is bound to a certain type of input data and whose variations allow for a better choice, the average permissible deviation of the initial weighting coefficients is determined.

To illustrate the application of a neural network in modeling economic processes, the data were used like during implementation of approximation and prediction module based on the adaptive statistical prediction method.

Let’s note that the construction of the forecast model is a process that continues the modeling procedure after the initial analysis of the graphical representation of the original data.
In order to determine the degree of preference of the method used with the received data type, the average deviation of the predicted value on predicted site is taken for both the adaptive statistical and the neural network method.

The results of experimental analysis showed that in most cases the model of statistical adaptive forecasting copes with the problem for a longer time interval than the neural network model. It was confirmed that the use of neural network models achieves high quality of time series approximation and forecasting of economic system behavior. The use of neural network models for business entities can provide a significant economic effect, since it allows to quickly and reliably predict the possible development of events in advance.

Key words: non-stationary economic process, time series, adaptive modeling, control, feedback, approximation, statistical prediction, neural network, standard deviation, training.

References

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Bibliographic reference: Akhatov A.R., Nazarov F.M. Development of the model for prediction the time series of non-stationary discrete systems on the basic of neuron network //journal “Problems of informatics”. 2018, № 3. P. 34-50.

article

Moiseenko V. V.

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

EXPERIENCE INFORMATIZATION OF THE SOVIET DISTRICT OF NOVOSIBIRSK IN 1970–1990 AND ITS FURTHER USE (RETROSPECTIVE REVIEW)

UDC 004.031.42

The article makes an excursion into the history of development and operation of territorial automated control systems (TASU) in the country. It is concluded that, despite a number of shortcomings, the operation of TACU has yielded positive results. The experience of informatization of the Soviet district of Novosibirsk is considered. Work on the design of the subsystems of the ACS of the Sovietsky District was launched on the initiative and under the guidance of the Chairman of the SB RAS, Academician G. Marchuk. In the mid-70’s.

In the design process, the research, design and organizational problems of creating the automated control system of the district were considered. The main functional subsystems of the district, subject to informatization, were identified. It is:
— Administration;
— Population;
— Health care;
— Housing and utilities;
— Capital construction

For each subsystem, the objectives of the creation of the automated control system are formulated, tasks to be solved with the help of automation, and indicators to be improved during the operation of subsystems.
The technical support of the automated control system of the district was the technical means of the Computing Center for Collective Use (VC KP), formed on the basis of the Computing Center of the Siberian Branch of the Academy of Sciences.

A brief description of other developments in informatization conducted by the staff of the Computing Center of the SB RAS is given. It:
— creation and maintenance of the database Cadres of the Institute;
— creation and maintenance of the database Scientific publications;
— with the use of these databases, the corresponding scientometric studies were carried out;
— development of a set of programs for the automated issuance of the first health insurance policies for employees of the SB RAS institutes.

Key words: urban area, territorial automated management system, district informatization, functional subsystems of the automated control system, distributed information-computing systems.

References

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Bibliographic reference: Moiseenko V.V. Experience informatization of the soviet district of Novosibirsk in 1970–1990 and its further use (retrospective review) //journal “Problems of informatics”. 2018, № 3. P. 51-60.

article

Narinyani A. S.

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

INTRODUCTION TO SUBDEFINITION

UDC 519.65: 519.85; 004.94; 004.82

Subdefinite models (S-models) are a new theory and technology of efficient solution of a wide range of problems from applied calculations to processing knowledge and problems of artificial intelligence. S-models refers to the direction of constraint programming, actively developed the last time in the world, as one of the most promising in IT. S-models qualitatively extends the possibilities of working with information and computational models of increased complexity, allowing to significantly simplify the process of creating next-generation systems and technologies, in particular in such areas as economics, management, complex objects and production processes management, engineering calculations and many others. S-models allow you to actively interact with the entire solution space, which in principle exceed the capabilities of traditional algorithmic methods and provide a qualitative leap in solving key problems in the development of modern information technologies.

Key words: computational models, constraint programming, method of subdefinite models, artificial intelligence, knowledge representation, NE-factors.

References

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Bibliographic reference: Narinyani A.S. Introduction to subdefinition //journal “Problems of informatics”. 2018, № 3. P. 61-81.

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2018 № 3(40)

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