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Function Approximation Performance of Fuzzy Neural Networks

TitleFunction Approximation Performance of Fuzzy Neural Networks
Publication TypeJournal Article
Year of Publication2010
AuthorsLovassy, R., L. T. Kóczy, and L. Gál
JournalActa Polytechnica Hungarica, Journal of Applied Sciences
Volume7
Issue 4
Pagination25-38
Date Published2010
Publication LanguageEnglish
ISSN Number1785-8860
Abstract

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.
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