On the problem of neural network decomposition into some subnets

Leonid B. Litinskii

Published 2000-01-18Version 1

An artificial neural network is usually treated as a whole system, being characterized by its ground state (the global minimum of the energy functional), the set of fixed points, their basins of attraction, etc. However, it is quite natural to suppose that a large network may consist of a set of almost autonome subnets. Each subnet works independently (or almost independently) and analyzes the same pattern from other points of view. It seems that it is a proper model for the natural neural networks. We discuss the problem of decomposition of a neural network into a set of weakly coupled subnets. The used technique is similar to the method for {\it the extremal grouping of parameters}, proposed by E.M.Braverman (1970).