Abstract
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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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