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 |
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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 | |
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