NDLI: A learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for associative memory applications

Content Provider	IEEE Xplore Digital Library
Author	Jui-Lin Lai Chung Yu Wu
Copyright Year	2004
Description	Author affiliation: Dept. of Electron. Eng. & Inst. of Electron., Nat. Chiao Tung Univ., Taiwan (Jui-Lin Lai; Chung Yu Wu)
Abstract	A self-feedback ratio-memory cellular nonlinear network (SRMCNN) with the B template and modified Hebbian learning algorithm to learn and recognize image patterns is proposed and analyzed. In this SRMCNN, the coefficients of space-variant B templates are determined from the exemplar patterns during the learning period. The weights are the ratio of the absolute summation of its neighborhood weights in the B templates stored in the associative memory. The SRMCNN can recognize the learned patterns with distinct white-black noise and output the correct patterns. Matlab and HSPICE software have simulated the operation of the proposed SRMCNN. It is shown that the 18/spl times/18 SRMCNN can successfully learn and recognize 8 incompletely noisy patterns. As compared to other learnable CNN as associative memories, the proposed SRMCNN could improve pattern learning and recognition capability. The architecture can be implemented in nano-CMOS technology for a giga-scale learning system in real-time applications.
Starting Page	183
Ending Page	186
File Size	753290
Page Count	4
File Format	PDF
ISBN	0780387155
DOI	10.1109/ICECS.2004.1399645
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-15
Publisher Place	Israel
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular networks Associative memory Pattern recognition Hebbian theory Image recognition Image analysis Pattern analysis Algorithm design and analysis Cellular neural networks Computer architecture
Content Type	Text
Resource Type	Article

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