Systems and Means of Informatics
2025, Volume 35, Issue 2, pp 17-30
MODELING ALGORITHMS FOR VECTOR STOCHASTIC PROCESS BY CANONICAL EXPANSIONS BASED ON MULTILAYER WAVELET NEURAL NETWORK
- I. N. Sinitsyn
- V. I. Sinitsyn
- E. R. Korepanov
- T. D. Konashenkova
Abstract
The paper is devoted to modeling methods and algorithms for vector stochastic process (StP) based on multilayer canonical expansions (CE) of wavelet neural network (WNN). Stochastic process is defined on a fixed time interval. Canonical expansion for matrix covariance functions construction is considered as approximation problem for elements of covariance functions by quadratic forms of basic wavelet with compact carriers. For its solution, multilayer architecture of WNN is developed. Training with teacher is realized by inverse error extension method. The CE of coordinate functions are taken in linear combination of basic wavelet functions with weighting coefficients whose optimal values are defined during WNN functioning. Special attention is paid to two-dimensional typical nonstationary StP. Advantages of CE of WNN algorithms are discussed.
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[+] About this article
Title
MODELING ALGORITHMS FOR VECTOR STOCHASTIC PROCESS BY CANONICAL EXPANSIONS BASED ON MULTILAYER WAVELET NEURAL NETWORK
Journal
Systems and Means of Informatics
Volume 35, Issue 2, pp 17-30
Cover Date
2025-05-20
DOI
10.14357/08696527250202
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
canonical expansion; covariance function; covariance matrix; modeling; stochastic process; wavelet; wavelet-neural network
Authors
I. N. Sinitsyn  , V. I. Sinitsyn  , E. R. Korepanov  , and T. D. Konashenkova
Author Affiliations
 Federal Research Center "Computer Science and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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