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Fuzzy regression with radial basis function network
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DOI：10.1016/S0165-0114(99)00098-6.png)
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Abstract
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Radial basis function network is used in fuzzy regression analysis without predefined functional relationship between the input and the output. The proposed approach is a fuzzification of the connection weights between the hidden and the output layers. This fuzzy network is trained by a hybrid learning algorithm, where self organized learning is used for training the parameters of the hidden units and supervised learning is used for updating the weights between the hidden and the output layers. The c-mean clustering method and the k-nearest-neighbor heuristics are used for the self-organized learning. The supervised learning is carried out by solving a linear possibilistic programming problem. Techniques for the generalization of the network are also proposed. Numerical examples are used to illustrate and to test the performances of the approach. (C) 2001 Elsevier Science B.V. All rights reserved.
Keywords:
regression analysis
nonparametric fuzzy regression
fuzzy radial basis network
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