عنوان
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Crisp set tuning of membership functions in fuzzy logic inference systems by probability density functions
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نوع پژوهش
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مقاله ارائه شده کنفرانسی
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کلیدواژهها
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Expert system, Fuzzification, Fuzzy role, Statistics
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چکیده
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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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پژوهشگران
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جلال خدائی (نفر سوم)، کاوه ملازاده (نفر دوم)، فرشید یاری (نفر اول)
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