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In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.

Tuning Method of the Membership Function for FLC using a Gradient Descent Algorithm