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The proposed approaches are backboned by the methods of fuzzy sliding-mode control (FSMC) and fuzzy neural network control (FNNC).
The calculus methods of fuzzy dimensions are examined and an application to the extreme positions of a rod-crank system with uncertain dimensions is also presented.
It uses the methods of fuzzy adaptive control, fuzzy logic approximation and fuzzy sliding mode control to approximate the ideal controller.
We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology.
In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming.
Subtractive clustering and Fuzzy C-Means are two basic methods of fuzzy clustering.
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The method of fuzzy ID3 decision tree has successfully been applied in this modeling procedure.
The paper describes a method of fuzzy model generation using numerical data as a starting point.
Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms.
The rule-based fuzzy model corresponds to the Takagi Sugeno Kang method of fuzzy reasoning proposed by Sugeno and his co-authors.
A simple and easy to apply method of fuzzy predictive control algorithms synthesis is presented in the paper.
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