Two simple strategies to improve bidirectional associative memory´s performance: unlearning and delta rule

This paper presents two strategies to improve the performance of the bidirectional associative memory (BAM). The unlearning of spurious attractors (USA-BAM) consists in dissociating any stimulus from an incorrect response. The bidirectional delta rule (BDR-BAM) extends the use of the delta rule to BAM bidirectional operation. These paradigms are based on cognitive assumptions, do not demand pre-processed inputs, train quickly the network, have stable behavior, and present high noise tolerance and abstraction ability. The models are compared with the original BAM and the pseudo-relaxation learning algorithm (PRLAB). A number of experiments suggest that the new methods present better performance than PRLAB when dealing with noisy input patterns. These three methods are combined two by two and the resulting model USA-BDR-BAM presents the best overall performance