This article is cited in 1 paper

Problems of Optimization and Simulation at Control of Development of Large-Scale Systems

Recurrent algorithms of structural classification analysis for complex organized information

A. A. Dorofeyuk^a, E. V. Bauman^a, J. A. Dorofeyuk^b, A. L. Chernyavskii<sup>b

a</sup> Markov Processes International, New York, USA
^b Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

Abstract: For the structural classification analysis of complex organized information, we propose to use recurrent algorithms of stochastic approximation type. We introduce classification quality functionals that depend on non-normalized and zero moments of probability distribution functions for the probability of sample objects appearing in the classes, as well as the type of optimal classification. We propose a new classification algorithm for this type of classification quality criteria and prove a theorem about its convergence that ensures the stationary value of the corresponding functional. We show that the proposed algorithm can be used to solve a wide class of problems in structural classification analysis.

Keywords: structural classification analysis of information, fuzzy classification, recurrent algorithms, stochastic approximation, fuzziness types, parameter structuring, cluster analysis, piecewise approximation of complex functions.

Presented by the member of Editorial Board: A. I. Mikhal'skii

Received: 09.11.2017

 English version: Automation and Remote Control, 2018, 79:10, 1854–1862

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