DocumentCode
2516746
Title
Global learning algorithms for discrete-time cellular neural networks
Author
Magnussen, Holger ; Nossek, Josef A.
Author_Institution
Inst. for Network Theory & Circuit Design, Tech. Univ. Munchen, Germany
fYear
1994
fDate
18-21 Dec 1994
Firstpage
165
Lastpage
170
Abstract
Two learning algorithms for discrete-time cellular neural networks (DTCNNs) are proposed, which do not require the a priori knowledge of the output trajectory of the network. A cost function is defined, which is minimized by direct search optimization methods and simulated annealing
Keywords
cellular neural nets; learning (artificial intelligence); simulated annealing; cost function; direct search optimization methods; discrete-time cellular neural networks; global learning algorithms; simulated annealing; Algorithm design and analysis; Cellular networks; Cellular neural networks; Circuit synthesis; Cost function; Data mining; Electronic mail; Equations; Neural networks; Optimization methods;
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.1994.381690
Filename
381690
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