Crisp set tuning of membership functions in fuzzy logic inference systems by probability density functions.
Determination of threshold values of membership functions is one of the key stages in design of a fuzzy logic inference system. This paper presents a statistical-based method using probability density functions to simplify the construction of membership functions and to fine-tune the critical points of the membership functions for the input variables. A Mamdani-type fuzzy inference system was developed to classify potato crop into healthy and damaged groups based on the image texture features. The results have shown a promising performance of the proposed method for design of fuzzy logic classifiers
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