An improved fuzzy identification method based on Sigmoid data transfer function

Unlike the traditional approaches that utilize original data patterns to construct the fuzzy model, an approach exploiting both data transformation techniques and heuristic method is proposed to simplify the modeling procedures. For the transferred data, firstly, the initial value of fuzzy if-then rules with nonfuzzy singletons (i.e., real numbers) in the consequent parts is generated by the heuristic method. Then, fine-tuning is done by gradient descent learning algorithm. The proposed method has better approximation accuracy and faster convergence speed. Simulation result demonstrates the superiority of the proposed model to the conventional methodologies.