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An Augmentation Learning Algorithm of Fuzzy Associative Memory

SHU Gui-qing (Dept.of Computer and Electronic Engi. Guangdong Prov. Institute for Tech. Personnel, Guangzhou 510640) XIAO Ping (Dept. of Electronic and Comm. Engi.,South China Univ. of Tech., Guangzhou 510641)  
This paper gives a new learning rule about the formation of weights for two-layer max-min feedforward fuzzy associative memory (FAM) network proposed by Kosko . Based on the new rule,The feedforward FAM model is developed into a fuzzy bidirectional associative memory (BAM) model,and a fuzzy quick augmentation algorithm is also proposed,Its stability and tolerance for the BAM model are also analyzed. From the analysis, an interesting result which can store an arbitrary given multi-value patterns is obtained. When used to store binary values, The weights for BAM model take binary too, 0 or 1.So it is suitable for the VLSI and optical implementation. In order to make a comparision, binary based sample patterns have adoped. A larger number of simulation results show the advantages of a less number of weighted value,or the simple implementation, by comparing with the existing learning algorithm,such as binary based Hoperfield dummy augmentation and MBDS augmentation algorithms. On the other hand, the fuzzy quick augmentation algirithm has the merit of the simpler computation and faster convergence.
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