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Proceedings Paper

Efficient learning algorithm for fuzzy max-product associative memory networks
Author(s): Ping Xiao; Ying Lin Yu
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Paper Abstract

A kind of fuzzy max-product associative memory network and its learning algorithm are presented in this paper. The connection weight matrix for fuzzy max-product auto- associative memory is determined by the generalized fuzzy solution. Each initial state pattern will be converged another state of the network via the connection weight matrix at one iteration. Fuzzy max-product associative memory network possess strong ability of error-tolerance. The computer simulations show the better performance of the fuzzy max-product associative memory network and its learning algorithm.

Paper Details

Date Published: 4 April 1997
PDF: 8 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271497
Show Author Affiliations
Ping Xiao, South China Univ. of Technology (China)
Ying Lin Yu, South China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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