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Recursive learning algorithms for training fuzzy recurrent models
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DOI：10.1002/(SICI)1098-111X(199612)11:12<1059::AID-INT3>3.0.CO;2-M.png)
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Abstract
En 中文
A recurrent fuzzy neural network with external feedback, called the Dynamical-Adaptive Fuzzy Neural Network (D-AFNN), is proposed in this article for adapting discrete-time dynamical systems. The fuzzy model is based on the Takagi-Sugeno inference method with polynomial consequent functions. The D-AFNN model is trained to learn the system dynamics using a recursive training algorithm. Both the epochwise and the online version of the recursive algorithm are considered. The standard representation of a dynamical system, using the fuzzy recurrent model, is discussed and the computational procedure for the derivation of the output gradients is analytically described. The performance qualities of the D-AFNN model are illustrated with two examples. In the first example, the model is trained to identify a nonlinear plant, while in the second the fuzzy recurrent model implements an IIR adaptive filter for noise attenuation. (C) 1996 John Wiley & Sons, Inc.
Keywords:
BACK-PROPAGATION
SYSTEMS
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