Designing bidirectional associative memories with optimal stability

In this paper, a learning algorithm for bidirectional associative memories (BAM´s) with optimal stability is presented. According to an objective function that measures the stability and attraction of the BAM, the authors cast the learning procedure into a global minimization problem, solved by a gradient descent technique. This learning rule guarantees the storage of training patterns with basins of attraction as large as possible. The authors also investigate the storage capacity of the BAR/L, the convergence of the learning method, the asymptotic stability of each training pattern and its basin of attraction. To evaluate the performance of the authors´ learning strategy, a large number of simulations have been carried out