An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization

Ryoma Bise; Norikazu Takahashi; Tetsuo Nishi

doi:10.1109/TCSI.2003.819827

An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization

Ryoma Bise, Norikazu Takahashi, Tetsuo Nishi

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1569-1574
Number of pages	6
Journal	IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume	50
Issue number	12
DOIs	https://doi.org/10.1109/TCSI.2003.819827
Publication status	Published - Dec 2003
Externally published	Yes

Keywords

Associative memory
Basin of attraction
Cellular neural networks (CNNs)
Generalized eigenvalue minimization (GEVM)

ASJC Scopus subject areas

Electrical and Electronic Engineering

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title = "An improvement of the design method of cellular neural networks based on generalized eigenvalue minimization",

abstract = "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.",

keywords = "Associative memory, Basin of attraction, Cellular neural networks (CNNs), Generalized eigenvalue minimization (GEVM)",

author = "Ryoma Bise and Norikazu Takahashi and Tetsuo Nishi",

note = "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.",

year = "2003",

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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

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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.

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KW - Basin of attraction

KW - Cellular neural networks (CNNs)

KW - Generalized eigenvalue minimization (GEVM)

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