An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization
Ryoma Bise, Norikazu Takahashi, Tetsuo Nishi · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 2003
Realization of associative memories by cellular neural networks (CNNs) with binary output is studied. Concerning this problem, a CNN design method based upon generalized eigenvalue minimization (GEVM) has recently been proposed. In this brief, a new CNN design method which is based on the GEVM-based method will be presented. We first give some analytical results related to the basin of attraction of a memory vector. We then derive the design method by combining these analytical results and the GEVM-based method. We finally show through computer simulations that the proposed method can achieve higher recall probability than the original GEVM-based method.