Weighted learning of bidirectional associative memories by global minimization

Abstract

A weighted learning algorithm for bidirectional associative memories (BAMs) by means of global minimization, where each desired pattern is weighted, is described. According to the cost function that measures the goodness of the BAM, the learning algorithm is formulated as a global minimization problem and solved by a gradient descent rule. The learning approach guarantees not only that each desired pattern is stored as a stable state, but also that the basin of attraction is constructed as large as possible around each desired pattern. The existence of the weights, the asymptotic stability of each desired pattern and its basin of attraction, and the convergence of the proposed learning algorithm are investigated in an analytic way. A large number of computer experiments are reported to demonstrate the efficiency of the learning rule.