Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint.

Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint. Research Abstract Details 

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Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint. Abstract Text:

B L Zhang,B Z Xu,C P Kwong,

This paper discusses the bidirectional associative memory (BAM) model from the matched-filtering viewpoint and offers it a new interpretation. Our attention is focused on the problem of stability and attractivity of equilibrium states. Several sufficient and/or necessary conditions are presented. To improve the BAM performance, an exponential function is used to enhance the correlations between the binary vectors of the retrieval key and that of the stored pattern similar to the key. The modified model is shown to be asymptotically stable. Theoretical analysis and simulation results demonstrate that the modified model performs much better than the original BAM in terms of memory capacity and error correction capability.

Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint. Publishing Authors By Initials

BL Zhang,BZ Xu,CP Kwong,

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Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: IEEE transactions on neural networks / a publicati

VOLUME: 4

Page Numbers: 864-72

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ISSN: 1045-9227

DAY: 15

MONTH: 02

YEAR: 1993

Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint. Information

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LANGUAGE: eng

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Grant and Affiliation Information for Performance analysis of the bidirectional associative memory and an improved model from the matched-filtering viewpoint.

AFFILIATION: Inst. of Electr. Eng. and Automatic Control, South China Univ. of Technol., Guangzhou.

Country: United States

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MEDLINETA: IEEE Trans Neural Netw

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