Bidirectional associative memory with learning capability using simultaneous perturbation

Authors:
Yutaka Maeda;Masatoshi Wakamura
Affiliations:
Department of Electrical Engineering and Computer Science, Faculty of Engineering, Kansai University, 3-3-35 Yamate-cho, Suita, Japan;Department of Electrical Engineering and Computer Science, Faculty of Engineering, Kansai University, 3-3-35 Yamate-cho, Suita, Japan
Venue:
Neurocomputing
Year:
2005

Citing 16
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Abstract

Bidirectional associative memory (BAM) is a typical recurrent network. It consists of two layers and can realize the hetero-associative memory in which recalled patterns are different from triggering patterns. Ordinarily, weights in the BAM are determined by Hebbian learning or the correlation learning for binary problems. In order to promote wider range of applications of the BAMs, it is crucial to invent new learning scheme which is applicable not only to the binary problems but also to analog ones. Moreover, hardware implementation of the BAMs with learning capability is intriguing. In this paper, a recursive learning scheme for the BAMs using the simultaneous perturbation is described. Moreover, its hardware realization using the FPGA is explained. Some results and the details of the realization are shown.