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The paper proposes a new fuzzy identification method based on H∞ error estimation for the issues of robust identification of fuzzy model. The H∞ state estimation is applied to the parameter identification of fuzzy model in the paper. The presented algorithm not only guarantees to satisfy a specified level of robustness, and also provides an optimized error upper bound. Finally, we study the fuzzy...
The paper presents a method for the identification of nonlinear system parameters by using an improved Genetic Algorithm and Grey Function. The paper firstly outlines several commonly used nonlinear identification methods such as RLS, RIV and COR and also their drawbacks. Then, a method based on the Genetic Algorithm and Grey Function is proposed and given in detail in the paper. Finally, a simulation...
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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