Recurrent Algorithms of Structural Classification Analysis for Complex Organized Information

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 probabili...

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Bibliographic Details
Published in:Automation and remote control Vol. 79; no. 10; pp. 1854 - 1862
Main Authors: Dorofeyuk, A. A., Bauman, E. V., Dorofeyuk, Yu. A., Chernyavskii, A. L.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.10.2018
Springer Nature B.V
Subjects:
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classification
Computer-Aided Engineering (CAD
Control
Distribution functions
Functionals
Mathematics
Mathematics and Statistics
Mechanical Engineering
Mechatronics
Probability distribution functions
Problems of Optimization and Simulation at Control of Development of Large-Scale Systems
Robotics
Systems Theory
fuzzy classification
fuzziness types
piecewise approximation of complex functions
recurrent algorithms
stochastic approximation
parameter structuring
cluster analysis
structural classification analysis of information
ISSN:0005-1179, 1608-3032
Online Access:Get full text
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Summary: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.
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ISSN:0005-1179
1608-3032
DOI:10.1134/S0005117918100090