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| Received:March 29, 2004Revised:April 20, 2005 |
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| Neuro-f uzzy system modeling based on automatic f uzzy clustering |
| Yuangang TANG , Fuchun SUN , Zengqi SUN |
| (Department of Computer Science and Technology , Tsinghua University ,Beijing 100084 , China) |
| Abstract: |
| A neuro-fuzzy system model based on automatic fuzzy clustering is proposed. A hybrid model identification algorithm is also developed to decide the model structure and model parameters. The algorithm mainly includes three parts :1) Automatic fuzzy C-means (AFCM) , which is applied to generate fuzzy rules automatically , and then fix on the size of the neuro-fuzzy network , by which the complexity of system design is reducesd greatly at the price of the fitting capability; 2)Recursive least square estimation ( RLSE) . It is used to update the parameters of Takagi-Sugeno model , which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network. Finally ,modeling the dynamical equation of the two- link manipulator with the proposed approach is illustrated to validate the feasibility of the method. |
| Key words: Neuro-fuzzy system Automatic fuzzy C-means Gradient descent Back propagation Recursive least square estimation Two- link manipulator |
