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Function Approximation Capability of a Novel Fuzzy Flip-Flop Based Neural Network

TitleFunction Approximation Capability of a Novel Fuzzy Flip-Flop Based Neural Network
Publication TypeConference Paper
Year of Publication2009
Publication LanguageEnglish
Pagination1900-1907
AuthorsLovassy, R., L. T. Kóczy, and L. Gál
Conference NameInternational Joint Conference on Neural Networks, IJCNN
Conference LocationAtlanta, USA
Abstract

The function approximation capability of various connectionist systems has been one of the most interesting problems. A method for constructing Multilayer Perceptron Neural Networks (MLP NN) with the aid of fuzzy operations based flip-flops able to approximate single and multiple variable functions is proposed. This paper introduces the concept of fuzzy flip-flop based neural network, particularly by deploying three types of fuzzy flip-flops as neurons. A comparative study of feedbacked fuzzy J-K and two kinds of fuzzy D flip-flops used as neurons, based on fuzzy algebraic, Yager, Dombi, Hamacher and Frank operations is given. Simulation results are presented for several test functions.