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Fuzzy Clustering Networks: Design Criteria for Approximation and Prediction
John MITCHELL Shigeo ABE
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1996/01/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
neural network, fuzzy logic, prediction, function approximation.,
Full Text: PDF(803.5KB)
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In previous papers the building of hierarchical networks made up of components using fuzzy rules was presented. It was demonstrated that this approach could be used to construct networks to solve classification problems, and that in many cases these networks were computationally less expensive and performed at least as well as existing approaches based on feedforward neural networks. It has also been demonstrated how this approach could be extended to real-valued problems, such as function approximation and time series prediction. This paper investigates the problem of choosing the best network for real-valued approximation problems. Firstly, the nature of the network parameters, how they are interrelated, and how they affect the performance of the system are clarified. Then we address the problem of choosing the best values of these parameters. We present two model selection tools in this regard, the first using a simple statistical model of the network, and the second using structural information about the network components. The resulting network selection methods are demonstrated and their performance tested on several benchmark and applied problems. The conclusions look at future research issues for further improving the performance of the clustering network.