TY - JOUR
T1 - An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization
AU - Bise, Ryoma
AU - Takahashi, Norikazu
AU - Nishi, Tetsuo
N1 - Funding Information:
Manuscript received September 20, 2002; revised December 28, 2002. This paper was presented in part at the 7th IEEE International Workshop on Cellular Neural Networks and their Applications, July 2002. This research was supported in part by the Japan Society for the Promotion of Science, Grant-in-Aid for Encouragement of Young Scientists 15760268 and in part by the 21st Century COE Program “Reconstruction of Social Infrastructure Related to Information Science and Electrical Engineering.” This paper was recommended by Associate Editor P. Arena.
PY - 2003/12
Y1 - 2003/12
N2 - 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.
AB - 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.
KW - Associative memory
KW - Basin of attraction
KW - Cellular neural networks (CNNs)
KW - Generalized eigenvalue minimization (GEVM)
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U2 - 10.1109/TCSI.2003.819827
DO - 10.1109/TCSI.2003.819827
M3 - Article
AN - SCOPUS:0742304101
VL - 50
SP - 1569
EP - 1574
JO - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
JF - IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
SN - 1057-7122
IS - 12
ER -