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Publications
| Publication name | ||||
|---|---|---|---|---|
| Technology development of goal programs based on object-oriented approach: Monograph. - Tomsk: Tomsk state university of control system and radioelectronics, 2007. – 207 p. | Silich M.P. | |||
| System Approach to Concept Development of Legal Base in Rendering Public Services // Bulletin of the Tomsk Polytechnic University. – 2006. – vol. 309 (num. 7). – P. 217-221. | Silich V.A., Silich M.P., Yavorsky M.I. | |||
| To develop the concept of legal base in rendering public services it is proposed for the first time to use system project technology of complex social-economic systems based on the object-oriented modelling method. The use of system project technology gives completeness and complexity of considering problems, purposes, principles, and means of providing citizens with guarantee of civil entitlement realization on public services. | ||||
| Development of tools for construction of intellectual object-oriented models to support decision making // Bulletin of the Tomsk Polytechnic University. – 2006. – vol. 309 (num. 7). – P. 165-169. | Starodubtsev G.V., Silich M.P., Silich V.A. | |||
| The architecture and realization of information system for decision making support on the bases of object-oriented models and functional dependences of attributes is suggested. The possibility of integration into the model of different methods of artificial intellect is shown | ||||
| Development of Program of energy security in the region // Polzunovsky Gazette. – 2006. - ¹1. – P. 200-204. | Silich M.P. | |||
| Fuzzy approximator of atmospheric temperature fields // Optoelectronics, Instrumentation and Data Processing, 2010, Vol. 46, No. 2, pp. 134–141. | Kataev M.Yu., Lavygina A.V., Khodashinskii I. A., Epshtein D.A. | |||
| Algorithms of constructing a fuzzy approximator of atmospheric temperature fields are considered. The consequent parameters of the fuzzy rule approximator are determined using the leastsquares method, and the antecedent parameters are determined by the genetic algorithm. Results of a numerical experiment are presented. Optimal approximator parameters are given. | ||||
| Fuzzy Approximator of Atmospheric Temperature Fields// Autometry -2010. V. 46, ¹ 2. pp. 39-48 | M. Y. Kataev, A. V. Lavygina, I. A. Khodashinskii, D. A. Epshtein | |||
| In this paper an algorithms to build a fuzzy system that approximates of an atmospheric temperature fields are investigates. For study fuzzy rules consequent of the least squares method, and for study antecedent – genetic algorithm are used. The results of numerical experiment are considered. The optimum parameters of approximator work are given. | ||||
| Bee and ant colony algorithms for fuzzy systems identification//Proceedings of TUSUR– 2009. – ¹ 2 (20). – pp. 157-161. | I.A. Hodashinsky, I.V. Gorbunov, P.A. Dudin | |||
| In this article, four techniques based on colony paradigms for fuzzy models identification are considered: discrete ant colony algorithm, continuous ant colony algorithm, direct ant colony algorithm, and bee colony algorithm. | ||||
| Parameters Identification of Singleton Fuzzy Models Based on Particle Swarm Techniques//Information technologies— 2009. — ¹6. — pp. 8 – 11. | Khodashinsky I. A. | |||
| In this paper we describe particle swarm techniques for parameters identification of fuzzy models. We present the results of computational experiments. | ||||
| Identification of fuzzy systems: methods and algorithms// Management issues. — 2009. — ¹ 4. —pp. 15-23. | Khodashinsky I.A. | |||
| The paper considers three basic phases of fuzzy systems construction: expert evaluation, structure identification, parameter estimation. Expert evaluation includes: selection of fuzzy model type; choice of t-normal functions to set the fuzzy logic operations; choice of a fuzzy logic inference. For structure identification the fuzzy clustering method and iterative algorithm are offered. For parameters optimization the following methods have been chosen: genetic algorithm, ant colony algorithm, particle swarm optimization, simulated annealing. | ||||
| Parametric Fuzzy Model Identification Based on a Hybrid Ant Colony Algorithm // Optoelectronics, Instrumentation and Data Processing, 2008, Vol. 44, No. 5, pp. 402–411. | Khodashinsky I. A. and Dudin P. A. | |||
| Applying the ant colony algorithm for solving the problem of parametric fuzzy model identification is presented. Transition from continuous optimization to discrete one via constructing a complete oriented decision-search graph is determined. A gradient algorithm is considered as the second optimization step. Experiments for analyzing the performance of the algorithms for optimization and fuzzy system are described. | ||||
| Parametric Fuzzy Model Identification Based on a Hybrid Ant Colony Algorithm// Autometry — 2008. —¹ 5, v. 44. — pp. 24-35. | Khodashinsky I. A. and Dudin P. A. | |||
| In this paper, the task of fuzzy model identification is formulated as an optimization problem and the features necessary for an ant colony algorithm is introduced. A hybrid optimization technique is proposed for continuous space optimization problems. The scheme incorporates a gradient descent into the ant colony algorithm. This hybrid method can improve the optimization performance and enhance the fast convergence during local search of the ant colony algorithm. Experiment results show the effectiveness and the applicability of the proposed algorithm. | ||||
| Soft value estimation techniques:monograph./Tomsk: Tomsk state university of control systems and radioelectronics, 2007. — 152 p. ISBN 978-5-86889-417-6 | I.A. Khodashinsky | |||
| Biology-inspired methods for parametrical identification of fuzzy models //Proceedings of TUSUR - 2007. – ¹ 2 (20). – pp. 81-92. | I.A. Khodashinsky, P.A. Dudin, A.V. Lavygina | |||
| In this article, three techniques based on biological paradigms are considered: genetic algorithm, ant colony algorithm and particle swarm optimization algorithm. The application features of the specified methods for fuzzy models identification are given. We also present the results of computational experiments with the models based on the specified methods. | ||||
| Formal logical reasoning approach and Mamdani's approximation in fuzzy estimation of values // Optoelectronics, Instrumentation and Data Processing, 2006, PART 1, pp. 47-58. | Khodashinsky I. A. | |||
| Formal logical reasoning approach and Mamdani`s approximation in fuzzy estimation of values | Khodashinsky I. A. | |||
| Fussy estimation procedures based on the formal logical reasoning approach and Mamdani`s approximation are considered. The inference quality was analyzed depending on the kind of membership function, the way of giving conjunction and disjunction operations, the way of giving the fuzzy inference, and the number of linguistic terms describing fuzzy estimates. | ||||
| Evaluating of Values: the Approach on the Basis of Soft Computing//Information technologies.-2006 ¹6.-pp.13-21 | Khodashinsky I. A. | |||
| The following methods of fuzzy evaluating of values are described: evaluating on the basis of fuzzy logic and on the basis of subjective probabilities. The questions of adjustment of fuzzy system are discussed by selection of expressions for operations of conjunction, disjunction, implication, parameters and kind of membership functions, number fuzzy rule. The following means of optimization of evaluating systems are submitted: back-propagation algorithm and genetic algorithm. | ||||
