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Automation and Remote Control

, Volume 79, Issue 10, pp 1854–1862 | Cite as

Recurrent Algorithms of Structural Classification Analysis for Complex Organized Information

  • A. A. Dorofeyuk
  • E. V. Bauman
  • Yu. A. Dorofeyuk
  • A. L. Chernyavskii
Problems of Optimization and Simulation at Control of Development of Large-Scale Systems
  • 3 Downloads

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 

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Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • A. A. Dorofeyuk
    • 1
  • E. V. Bauman
    • 1
  • Yu. A. Dorofeyuk
    • 2
  • A. L. Chernyavskii
    • 2
  1. 1.Markov Processes InternationalNew YorkUSA
  2. 2.Trapeznikov Institute of Control SciencesRussian Academy of SciencesMoscowRussia

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