Introduction: Basic concepts of Neural Networks and Fuzzy Logic, Differences between conventional computing and Neuro-Fuzzy computing, Characteristics of Neuro-Fuzzy computing Fuzzy Set Theory: Basic definitions and terminology and membership functio...See more

ns – Formulation and parameters, basic operations of fuzzy sets – complement, intersection vision, T-norm and Tconorm
Introduction Fuzzy Sets and Fuzzy Logic Fuzzy sets were introduced by Zadeh in 1965 to represent/manipulate data and information possessing non-statistical uncertainties. It was specifically designed to mathematically represent uncertainty and vagueness and to provide formalized tools for dealing with the imprecision intrinsic to many problems. However, the story of fuzzy logic started much more earlier . To devise a concise theory of logic, and later mathematics, Aristotle posited the so-called‖Laws of Thought‖.

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Introduction: Basic concepts of Neural Networks and Fuzzy Logic, Differences between conventional computing and Neuro-Fuzzy computing, Characteristics of Neuro-Fuzzy computing Fuzzy Set Theory: Basic definitions and terminology and membership functions – Formulation and parameters, basic operations of fuzzy sets – complement, intersection vision, T-norm and Tconorm
Introduction Fuzzy Sets and Fuzzy Logic Fuzzy sets were introduced by Zadeh in 1965 to represent/manipulate data and information possessing non-statistical uncertainties. It was specifically designed to mathematically represent uncertainty and vagueness and to provide formalized tools for dealing with the imprecision intrinsic to many problems. However, the story of fuzzy logic started much more earlier . To devise a concise theory of logic, and later mathematics, Aristotle posited the so-called‖Laws of Thought‖.

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Fuzzy sets theory of Artificial Intelligence is described here thoroughly

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