Fuzzy Modeling with Fuzzy Adaptive Simulated Annealing
Data-based fuzzy system modeling usually depends on effective optimization methods to fit experimental data to parametric fuzzy models. Here, an approach that uses Takagi-Sugeno models and Adaptive Simulated Annealing (ASA) is presented and discussed, showing that (Fuzzy) ASA could also be helpful in such a kind of task. The problem to solve is well-defined - given a training set containing a finite number of input-output pairs, construct a fuzzy system approximating the behavior of the actual system that originated that set, within a pre-established precision. Such an approximation must have generalization ability to be useful in the real world, considering the finiteness of the training set and other constraints. Besides, other suggestions for application of (Fuzzy) ASA to fuzzy logic related problems are offered.
KeywordsFuzzy System Cluster Center Fuzzy Modeling Fuzzy Cluster Very High
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