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Fuzzy Flip-Flop Based Neural Network as a Function Approximator

TitleFuzzy Flip-Flop Based Neural Network as a Function Approximator
Publication TypeConference Paper
Year of Publication2008
Publication LanguageEnglish
Pagination44-49
AuthorsLovassy, R., L. T. Kóczy, and L. Gál
Conference NameIEEE International Conference on Computational Intelligence for Measurment Systems and Applications CIMSA
Conference LocationIstanbul, Turkey
Abstract

Artificial neural networks and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A family of fuzzy flip-flops is proposed, based on an artificial neural network-like structure which is suitable for approximating many-input one-output nonlinear functions. The neurons in the multilayer perceptron networks typically employ sigmoidal activation functions. The next state of the fuzzy J-K flip-flops (F3) using Yager and Dombi operators present quasi-S-shaped characteristics. The paper proposes the investigation of the possibility of constructing multilayer perceptrons from such fuzzy units. Each of the two candidates for F3-based neurons is examined for its training capability by evaluating and comparing the approximation properties in the context of different transcendental functions with one-input and multi-input cases. Simulation results are presented.
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