Readings in Fuzzy Sets for Intelligent Systems 1993
DOI: 10.1016/b978-1-4832-1450-4.50045-6
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Fuzzy Identification of Systems and Its Applications to Modeling and Control

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Cited by 976 publications
(1,197 citation statements)
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“…A FIS consists of five major steps: fuzzification of input variables, application of fuzzy operators (AND, OR, and NOT) in the rule's antecedent, implication from the antecedent to the consequent, aggregation of consequent across the rules, and defuzzification (Asoodeh and Bagheripour 2012a). Defining appropriate membership functions (fuzzy sets) which best fit the data set is an important task that is done by subtractive clustering algorithm in Takagi and Sugeno (1985) FIS. Subtractive clustering is an effective approach to estimate the number of fuzzy clusters and cluster centers in Takagi-Sugeno fuzzy inference system (Jarrah and Halawani 2001).…”
Section: Fuzzy Logicmentioning
confidence: 99%
“…A FIS consists of five major steps: fuzzification of input variables, application of fuzzy operators (AND, OR, and NOT) in the rule's antecedent, implication from the antecedent to the consequent, aggregation of consequent across the rules, and defuzzification (Asoodeh and Bagheripour 2012a). Defining appropriate membership functions (fuzzy sets) which best fit the data set is an important task that is done by subtractive clustering algorithm in Takagi and Sugeno (1985) FIS. Subtractive clustering is an effective approach to estimate the number of fuzzy clusters and cluster centers in Takagi-Sugeno fuzzy inference system (Jarrah and Halawani 2001).…”
Section: Fuzzy Logicmentioning
confidence: 99%
“…[25,26]. Once the optimal consequent parameters have been found, the backward pass starts immediately.…”
Section: Hybrid Learning Algorithm Of Anfismentioning
confidence: 99%
“…The Takagi -Sugeno (T S) fuzzy model approach has nowadays become popular since [1][2] [6], it showed its efficiency to control complex nonlinear systems with uncertainties parameters and used for many applications [7]. This fuzzy dynamic model is a piecewise interpolation of several linear models through membership functions [4].…”
Section: Introductionmentioning
confidence: 99%