Towards a learning algorithm for discrete-time cellular neural networks

Holgcr Magnussen, Josef A. Nossek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

The learning process for a Discrete-Time Cellular Neural Network is formulated as an optimization problem. This involves minimizing an objective function, which is a measure of the errors in the desired input-to-output image mapping process performed by the network. With this approach, the learning algorithm finds the trajectories, so they no longer have to be designed by the user.

Original languageEnglish
Title of host publicationProceedings - 2nd International Workshop on Cellular Neural Networks and their Applications, CNNA 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages80-85
Number of pages6
ISBN (Electronic)0780308751, 9780780308756
DOIs
StatePublished - 1992
Event2nd International Workshop on Cellular Neural Networks and their Applications, CNNA 1992 - Munich, Germany
Duration: 14 Oct 199216 Oct 1992

Publication series

NameProceedings - 2nd International Workshop on Cellular Neural Networks and their Applications, CNNA 1992

Conference

Conference2nd International Workshop on Cellular Neural Networks and their Applications, CNNA 1992
Country/TerritoryGermany
CityMunich
Period14/10/9216/10/92

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