Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Complex systems modeling via fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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An automated method to generate fuzzy rules and membership functions from a set of sample data is presented. Our method is based on clustering and uses a modified version of Gustafson-Kessel algorithm. The aim is to divide a product space into set of clusters for which the systems exhibits behavior close to linear. For each of the clusters we produce a fuzzy rule and generate a set of membership functions for the rule antecedent with use of an approach based on curve fitting. Weighted linear least-squares regression is used to obtain consequent functions for TSK-models.