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- W2387941851 abstract "The regression accuracy and generalization performance of SVR models depend on a proper setting of its parameters. This paper proposed an optimal selection approach of SVR parameters based on improved FS algorithm and applied on traffic flow forecasting. FS algorithm is a novel evolutionary method,and improved the decision mechanism of individual initial position through introducing catastrophe strategy based on relative density degree to extend the area of feasible solutions and enhanced the global search ability. Through a rolling forecasting simulation experiment on real traffic volume,the experimental results show that the proposed method is feasible and effective for the optimal selection of SVR parameters,and has better generalized performance and prediction accuracy than rule of thumb and genetic algorithm." @default.
- W2387941851 created "2016-06-24" @default.
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- W2387941851 date "2009-01-01" @default.
- W2387941851 modified "2023-09-27" @default.
- W2387941851 title "Parameters selection of support vector regression by improved FS algorithms and its application" @default.
- W2387941851 hasPublicationYear "2009" @default.
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