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Robust neural-fuzzy method for function approximation
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DOI：10.1016/j.eswa.2008.08.072.png)
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The back propagation (BP) algorithm for function approximation is multilayer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least Squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter is used to partition the nonlinear system's domain into several piecewise linear subspaces to be represented by neural networks. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm is proposed to greatly mitigate the influence of data noise and outliers. (2) A fuzzy-based data sifter is proposed to locate good turning-points to partition a given nonlinear data domain into piecewise clusters so that a neural network can be constructed with fewer rules. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers. Crown Copyright (c) 2008 Published by Elsevier Ltd. All rights reserved.
Keywords:
Back propagation algorithm
Fuzzy neural networks
Fuzzy clustering
Robust
Noises and outliers
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