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- W2028817587 abstract "A new fuzzy identification approach using support vector regression (SVR) and immune clone selection algorithm (ICSA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved ICSA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability." @default.
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- W2028817587 date "2009-06-01" @default.
- W2028817587 modified "2023-09-27" @default.
- W2028817587 title "A new fuzzy identification approach using support vector regression and immune clone selection algorithm" @default.
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- W2028817587 doi "https://doi.org/10.1109/ccdc.2009.5192744" @default.
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