Function Approximation Performance of Fuzzy Neural Networks
نویسندگان
چکیده
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 a real-life application, two input trigonometric functions, and 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.
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