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In the paper, the structure determination and parameter estimation for the non-linear systems are presented by means of the dynamic fuzzy model. The parameters estimation of fuzzy model is independent of each other by means of the orthogonal method. The most significant fuzzy rules are selected into the fuzzy model based on the “Innovation-Contribution” criterion and some other information criteria...
A novel T-S fuzzy control algorithm is presented for active queue management(AQM) in order to solve the problem of congestion control in TCP communication. This kind of control action has robust performance, which is suitable for time varying and complex network systems. For the particular TCP network mode, a T-S fuzzy model is done for the nonlinear TCP/IP network congestion control system. The performance...
First of all, competitive learning takes place in the product space of systems inputs and outputs and each cluster corresponds to a fuzzy IF-THEN rule. Fuzzy relation matrix confirmed by fuzzy competitive learning is studied by orthogonal least square algorithm. The validity of fuzzy rules is obtained by means of analyzing the efforts of orthogonal vectors in fuzzy model, and subsequently removes...
The fuzzy modeling method with singular value decomposition (SVD) is proposed in the paper. First of all, the fuzzy clustering is utilized to define the input space of fuzzy model. In addition, the recursive Kalman filtering algorithm with singular value decomposition is used to confirm the conclusion parameters of fuzzy model for the sake of accumulating and transferring of the errors. The parameters...
In this paper, we proposed a learning algorithm for fuzzy modeling based on the improved fuzzy clustering method and QR decomposition. The improved fuzzy clustering method is confirmed by using a new objective function, which includes the influence on the input variables and the output variables exerting the input space of fuzzy model. Fuzzy inference matrix acquired from improved fuzzy clustering...
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