Abstract
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.
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© 1999 Springer-Verlag Wien
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Hasegawa, K., Hattori, M. (1999). Improved Pseudo-Relaxation Learning Algorithm for Robust Bidirectional Associative Memory. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_48
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_48
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
eBook Packages: Springer Book Archive