Analysis and design of associative memories based on stability of cellular neural networks

Authors:
Qi Han;Xiaofeng Liao;Tingwen Huang;Jun Peng;Chuandong Li;Hongyu Huang
Affiliations:
School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China;State Key Laboratory of Power Transmission Equipment and System Security, College of Computer Science, Chongqing University, Chongqing 400030, China;Texas A&M University at Qatar, Doha, P.O. Box 23874, Qatar;School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, China;State Key Laboratory of Power Transmission Equipment and System Security, College of Computer Science, Chongqing University, Chongqing 400030, China;State Key Laboratory of Power Transmission Equipment and System Security, College of Computer Science, Chongqing University, Chongqing 400030, China
Venue:
Neurocomputing
Year:
2012

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Abstract

It is well known that the prototype patterns in associative memories can be represented by stable equilibrium points of cellular neural networks (CNNs). Therefore, the stability of equilibrium points of CNNs is critical in associative memories based on CNNs. In this paper, some criteria about the stability of CNNs are established. In fact, these criteria give some constraint conditions for the relationship of parameters of CNNs. Compared with the previous works, our results relax the conservatism of the relationship of parameters and extend the range of the values of parameters. Two design procedures on the parameters of CNNs are given to achieve associative memories under our criteria. Finally, an example is given to verify the theoretical results and design procedures.