DocumentCode
2516732
Title
Continuation-based learning algorithm for discrete-time cellular neural networks
Author
Magnussen, Holger ; Papoutsis, Georgiog ; Nossek, Josef A.
Author_Institution
Inst. for Network Theory & Circuit Design, Tech. Univ. Munchen, Germany
fYear
1994
fDate
18-21 Dec 1994
Firstpage
171
Lastpage
176
Abstract
The SGN-type nonlinearity of a standard discrete-time cellular neural network (DTCNN) is replaced by a smooth, sigmoidal nonlinearity with variable gain. Therefore, the resulting dynamical system is fully differentiable. Bounds on gain of the sigmoidal function are given, so that the new smooth system approximates the standard DTCNN within certain limits. A learning algorithm is proposed, which finds the template parameters for the standard DTCNN by gradually increasing the gain of the sigmoidal function
Keywords
cellular neural nets; learning (artificial intelligence); continuation-based learning algorithm; discrete-time cellular neural networks; sigmoidal function; sigmoidal nonlinearity; template parameters; Cellular networks; Cellular neural networks; Circuit synthesis; Electronic mail; Integrated circuit interconnections;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on
Conference_Location
Rome
Print_ISBN
0-7803-2070-0
Type
conf
DOI
10.1109/CNNA.1994.381689
Filename
381689
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