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Identification of fuzzy model using evolutionary programming and least squares estimate

A novel hybrid algorithm EPLSE is proposed 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/sup st/-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 and simplified its structure. 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.