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A saturation based interpolation method for fuzzy systems

Abstract This paper presents an alternative inference-defuzzification algorithm for Takagi-Sugeno fuzzy systems that preserves local-model interpretation and convexity properties. The linear model in the rule consequent is saturated outside the core set of the antecedent membership functions. This allows the interpretation of the consequents of fuzzy rules as a local linearization of the model restricted to the subset where it is valid. The setting has readability advantages over Takagi-Sugeno frameworks, and it is simpler that other interpolation proposals. Some examples illustrate the approach.