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This Article presents an alternative for traditional inference mechanisms and most typically, the compositional rule of inference (CRI) method. The most important benefit of the new method is its higher robustness with respect to changes in the rule base and its ability to function when the latter is sparse. The proposed method is based on a fuzzy generalization of the ordinary least-square interpolation method. The authors consider the degradation caused by elimination of a rule I a fuzzy controller and show that CRI is more sensitive than their interpolation-based method.
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