NDLI: Fuzzy flip-flop based neural network as a function approximator

Content Provider	IEEE Xplore Digital Library
Author	Lovassy, R. Koczy, L.T. Gal, L.
Copyright Year	2008
Description	Author affiliation: Inst. of Inf. Technol., Mech. & Electr. Eng., Szechenyi Istvan Univ., Gyor (Lovassy, R.)
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 $(F^{3})$ 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 $F^{3}-based$ 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.
Starting Page	44
Ending Page	49
File Size	1696599
Page Count	6
File Format	PDF
ISBN	9781424423057
DOI	10.1109/CIMSA.2008.4595830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-14
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Neurons Flip-flops Function approximation Artificial neural networks Simulation Training learning systems J-K fuzzy flip-flop fuzzy flip-flop based neural network function approximation
Content Type	Text
Resource Type	Article

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