Result: Pattern memory and acquisition based on stability of cellular neural networks
Title:
Pattern memory and acquisition based on stability of cellular neural networks
Authors:
Source:
2004 International Joint Conference on Neural Networks (proceedings).
Publisher Information:
Piscataway NJ: IEEE, 2004.
Publication Year:
2004
Physical Description:
print, 13 ref 4
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Intelligence artificielle, Artificial intelligence, Perceptron, Radiotéléphonie cellulaire, Cellular radio, Réseau cellulaire, Cell network, Red celular, Réseau neuronal, Neural network, Red neuronal, Système modulaire, Modular system, Sistema modular, Algorithme cellulaire, Cellular algorithm, Algoritmo celular
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O.Box 1130, Hefei Anhui 230031, China
Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China
Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230026, China
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17623583
Database:
PASCAL Archive
Further Information
In this paper, some sufficient conditions are obtained to guarantee that the n-dimensional cellular neural networks can have even (≤ 2) memory patterns. And we have obtained the estimates of attracting domain of such stable memory patterns. Those conditions directly derived from the parameters of the neural networks, are very easy to verified. A new design algorithm for cellular neural networks is developed based on stability theory (not base on the well-known perceptron training algorithm), and the convergence of the design algorithm is guaranteed by some stability Theorems. The results presented in this paper are new. Finally, the validity and performance of the results are illustrated by simulation results.