A Novel Fuzzy Modeling Strategy Using Evolutionary Programming and Least Squares Estimate
نویسندگان
چکیده
In designing a fuzzy model to a complex system, we encounter a major difficulty in the identification of an optimised fuzzy rule base, which is traditionally achieved by a tedious trial and error process. This paper has proposed a novel hybrid algorithm EPLSE to design fuzzy rule bases automatically, which is based on the combination of EP (Evolutionary Programming) and LSE (Least Squares Estimate). By utilizing the consequent parameters of the super 1-order Sugeno model, the training error is decreased greatly. Compared with the original work, the proposed algorithm has remarkably improved the fuzzy model’s precision. In the simulation, EPLSE is employed to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed. Other promising applications of the proposed EPLSE are also suggested.
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