Improved Pseudo-Relaxation Learning Algorithm for Robust Bidirectional Associative Memory

摘要

In this paper, we propose Improved Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory (IPRLAB). Since the proposed IPRLAB is based on the conventional PRLAB, it can guarantee the recall of all training pairs and has high storage capacity. Furthermore, the proposed IPRLAB can much improve the noise reduction effect of the BAM and contribute to construct a robust memory. A number of computer simulation results show the effectiveness of the proposed learning algorithm.