Global learning algorithms for discrete-time cellular neural networks

Holger Magnussen, Josef A. Nossek

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

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. Applications of the algorithms are presented in a companion paper.

Original languageEnglish
Pages165-170
Number of pages6
StatePublished - 1994
EventProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) - Rome, Italy
Duration: 18 Dec 199421 Dec 1994

Conference

ConferenceProceedings of the 3rd IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)
CityRome, Italy
Period18/12/9421/12/94

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