Cone Fuzzy Systems and Their Application to Modeling Nonlinear Systems
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Fuzzy systems can effectively model complex nonlinear systems with uncertainties, fuzzy system can make full use of the experience knowledge of domain experts and make it easy to understand. Theoretically, the modeling precision of fuzzy model is related to the number of fuzzy rules. The more fuzzy rules, the higher the precision of the fuzzy model. However, in practice, when modeling fuzzy models for some nonlinear systems, the improvement of fuzzy model precision is limited by simply increasing the number of fuzzy rules, which, on the contrary, greatly increases the calculation amount of the...
Fuzzy systems can effectively model complex nonlinear systems with uncertainties, fuzzy system can make full use of the experience knowledge of domain experts and make it easy to understand. Theoretically, the modeling precision of fuzzy model is related to the number of fuzzy rules. The more fuzzy rules, the higher the precision of the fuzzy model. However, in practice, when modeling fuzzy models for some nonlinear systems, the improvement of fuzzy model precision is limited by simply increasing the number of fuzzy rules, which, on the contrary, greatly increases the calculation amount of the entire modeling process, leading to redundancy of the fuzzy rules and overfitting of the models, so the balance and compromise between complexity and precision has become a hot issue in the research of fuzzy system identification. Therefore, how to design a simple and effective fuzzy system, improve its approximation performance and reduce its computational complexity become the main starting points of this paper.
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