Abstract
This paper presents a new approach for fuzzy rule base reduction using similarity concepts and interpolation techniques. The algorithm consists on: First, measure similarity between rules for the best choice of which of them will be deleted. This operation is done without modification of membership functions. Second, if a new input data is presented to the fuzzy system, interpolation techniques will be used to take into account this arriving data. The main idea of this work is to improve accuracy of the fuzzy system after reduction step. A comparative study between three interpolation methods is done. A mathematical case is treated to show the performance of the proposed method.
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