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A simple algorithm for training fuzzy systems using input-output data
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DOI：10.1016/S0965-9978(03)00034-6.png)
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This paper proposes a simple algorithm for training fuzzy systems from numerical data: The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method. (C) 2003 Elsevier Science Ltd. All rights reserved.
Keyword:
training fuzzy systems
fuzzy basis function
nearest neighbor algorithm
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