ISSN 1817-2172, рег. Эл. № ФС77-39410, ВАК

Differential Equations and Control Processes
(Differencialnie Uravnenia i Protsesy Upravlenia)

Randomized Unsupervized Algorithms for Neural Networks


O. Izmakova

Russia, 199034, St. Petersburg, Universitetskaya nab., 7/9,
St.Petersburg State University,
Department of Mathematics and Mechanics,


Two new unsupervised learning algorithms are described in this paper. They belong to the class of randomized stochastic algorithms.
The conditions for conver\-gence of estimates to the true values of unknown parameters are established for both algorithms.
The problem of the Hebb-Hopfield networks learning is discussed as an example. The method is given to solve the problem. It is based on the variational approach and randomized unsupervised algorithm.

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