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Function Approximation Performance of Fuzzy Neural Networks

Author(s): Rita Lovassy | László T. Kóczy | László Gál

Journal: Acta Polytechnica Hungarica
ISSN 1785-8860

Volume: 7;
Issue: 4;
Start page: 25;
Date: 2010;
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Keywords: fuzzy flip-flop neurons | Fuzzy Neural Networks | Bacterial Memetic Algorithm with Modified Operator Execution Order

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.
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