Applying bacterial memetic algorithm for training feedforward and fuzzy flip-flop based neural networks

Gál, László; Botzheim, J.; Kóczy, László T.; Ruano, Antonio

http://hdl.handle.net/10400.1/2143

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Abstract(s)

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.

Keywords

Bacterial Memetic Algorithm Fuzzy Flip-Flop Levenberg-Marquardt method Neural Network

URI

http://hdl.handle.net/10400.1/2143

Citation

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.

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