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Pattern recognition methods as applied to optimize systems represented by simulation models

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Abstract

This overview lecture discusses four topics and describes the method of approximate optimization of dynamic stochastic systems known as the optimization-simulation method. The grid method of uniform probing of the space of parameters called LP τ-search with averaging allows estimating the efficiency region as the region in the space of parameters where the system quality indices are better than in other regions. Ensuring better quality of processing the results of simulation experiments, pattern recognition methods allow speeding up the evaluation of efficiency regions.

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Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, ul. Profsoyuznaya 65, Moscow, 117997, Russia

    G. M. Antonova

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  1. G. M. Antonova
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Correspondence to G. M. Antonova.

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Galina M. Antonova. Born in 1951. Graduated from St Petersburg State Polytechnic University (Leningrad Polytechnic University) in 1974. Received candidate’s degree in Technical Cybernetics and Information Theory in 1980. She completed her second thesis and received her doctor’s degree in System Analysis, Control, and Information Processing at Trapeznikov Institute of Control Sciences in 2002. Her further work deals with improving the proposed methods and algorithms of optimization of dynamic stochastic systems using simulation models. At present, leading scientist at Trapeznikov Institute of Control Sciences. Member of Moscow Society for Simulation Modeling and the Mendeleev Russian Chemical Society. Scientific interests include simulation modeling, optimization of dynamic stochastic systems, pattern recognition. Author of more than 150 publications, including 3 monographs.

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Antonova, G.M. Pattern recognition methods as applied to optimize systems represented by simulation models. Pattern Recognit. Image Anal. 22, 69–81 (2012). https://doi.org/10.1134/S105466181201004X

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