Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2143
Título: Applying bacterial memetic algorithm for training feedforward and fuzzy flip-flop based neural networks
Autor: Gál, László
Botzheim, J.
Kóczy, László T.
Ruano, A. E.
Palavras-chave: Bacterial Memetic Algorithm
Fuzzy Flip-Flop
Levenberg-Marquardt method
Neural Network
Data: 2009
Citação: Gál, László; Botzheim, Janos; Koczy, Laszlo T.; Ruano, A. E. Applying Bacterial Memetic Algorithm for Training Feedforward and Fuzzy Flip-Flop based Neural Networks, Trabalho apresentado em Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, In Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, 2009.
Resumo: In our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memetic Algorithm with Modified Operator Execution Order for training feedforward and fuzzy flipflop based neural networks. We found that training these types of neural networks with the adaptation of the method we had used to train fuzzy rule bases had advantages over the conventional earlier methods.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2143
ISBN: 978-989-95079-6-8
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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