Polynomial discrete time Cellular Neural Networks to solve the XOR problem

Eduardo Gomez-Ramirez, Giovanni Egidio Pazienza, Xavier Vilasis-Cardona

Producció científica: Capítol de llibreContribució a congrés/conferènciaAvaluat per experts

5 Cites (Scopus)

Resum

Some papers discuss different option to improve the capabilities of Cellular Neural Networks (CNN), The principal point is that a single layer CNN can not solve problems with linearly nonsepurable data. In this paper a new model called Polynomial Discrete Time Cellular Neural networks is presented. This model has a very simple nonlinear term that can improve the performance of the network. The results show how it is possible to solve the XOR problem. The templates of the entire network are computed using genetic algorithm,

Idioma originalAnglès
Títol de la publicacióProceedings of the 2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006
DOIs
Estat de la publicacióPublicada - 2006
Esdeveniment2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006 - Istanbul, Turkey
Durada: 28 d’ag. 200630 d’ag. 2006

Sèrie de publicacions

NomProceedings of the IEEE International Workshop on Cellular Neural Networks and their Applications

Conferència

Conferència2006 10th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA 2006
País/TerritoriTurkey
CiutatIstanbul
Període28/08/0630/08/06

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