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Function Approximation Performance of Fuzzy Neural Networks | science.uni-obuda.hu
Acta Polytechnica Hungarica, Journal of Applied Sciences
Évfolyam
7
Kötet
4
Oldalszám
25-38
Kiadás dátuma
2010
Kiadás nyelve
English
ISSN Number
1785-8860
Összefoglalás
In this paper we propose a Multilayer Perceptron Neural Network (MLP NN) consisting of fuzzy flip-flop neurons based on various fuzzy operations applied in order to approximate real life application, two input trigonometric functions furthermore two and six dimensional benchmark problems. The Bacterial Memetic Algorithm with Modified Operator Execution Order algorithm (BMAM) is proposed for Fuzzy Neural Networks (FNN) training. The simulation results showed that various FNN types delivered very good function approximation results.
