標題: Architectural design and analysis of learnable self-feedback ratio-memory cellular nonlinear network (SRMCNN) for nanoelectronic systems
作者: Lai, JL
Wu, PCY
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: cellular nonlinear network;modified Hebbian learning algorithm;nanoelectronic;ratio memory;template
公開日期: 1-十一月-2004
摘要: In this paper, a learnable cellular nonlinear network (CNN) with space-variant templates, ratio memory (RM), and modified Hebbian learning algorithm is proposed and analyzed. By integrating both the modified Hebbian learning algorithm with the self-feedback function and a ratio memory into CNN architecture, the resultant ratio-memory (RMCNN) is called the self-feedback RMCNN (SRMCNN) which can serve as the associative memory. It can generate the absolute weights and then transform them into the ratioed A-template weights as the ratio memories for recognizing noisy input patterns. Simulation results have shown that with the stronger feature enhancement effect, the SRMCNN under constant leakage current can store and recognize more patterns than the RMCNN. For 18 x 18 SRMCNN, 93 noisy patterns with a uniform distribution noise level of 0.8 and a variance of normal distribution noise of 0.3 can be learned, stored, and recognized with 100% success rate. The SRMCNN has greater learning and recognition capability when the learned patterns are simpler and the noise is lower. For the learning and recognition of complicated patterns, the allowable pattern number is decreased for a 100% success rate. Simulation results have successfully verified the correct functions and better performance of SRMCNN in the pattern recognition. With high integration capability and excellent pattern association performance, the proposed SRMCNN can be applied to nanoelectronic associative-memory systems for image processing applications.
URI: http://dx.doi.org/10.1109/TVLSI.2004.836309
http://hdl.handle.net/11536/25719
ISSN: 1063-8210
DOI: 10.1109/TVLSI.2004.836309
期刊: IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
Volume: 12
Issue: 11
起始頁: 1182
結束頁: 1191
顯示於類別:期刊論文


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  1. 000224899800007.pdf