Function Approximation Performance of Fuzzy Neural Networks

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 toapproximate a real-life application, two input trigonometric functions, and two and sixdimensional benchmark problems. The Bacterial Memetic Algorithm with ModifiedOperator Execution Order algorithm (BMAM) is proposed for Fuzzy Neural Networks(FNN) training. The simulation results showed that various FNN types delivered very goodfunction approximation results.