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题名: A taylor series based input variable selection for nonlinear system
作者: Song GW(宋广为) ; Li DQ(李德强)
作者部门: 光电信息技术研究室
会议名称: 11th Joint International Computer Conference, JICC 2005
会议日期: November 10-12, 2005
会议地点: Chongqing, China
会议主办者: China Computer Federation; Hong Kong Computer Society
会议录: Proceedings of the 11th Joint International Computer Conference, JICC 2005
会议录出版者: World Scientific Publishing Co. Pte Ltd
会议录出版地: SINGAPORE
出版日期: 2005
页码: 542-547
收录类别: CPCI(ISTP) ; EI
ISBN号: 9812565329
关键词: Forestry ; Taylor series
摘要: Input variable selection is always a significant problem in nonlinear system modeling. In this paper, we propose an effective and efficient input variable selection method based on Taylor series. We assume the nonlinear system as an at least 3 order derivative and continuous function f(x), and approximate it with 3 order Taylor series in the small proximities of the sample data. We define the average absolute values of first order partial derivates at the sample data as the importance indexes for the input variable candidates, and the average absolute values of second order partial derivatives as the correlation degrees between the input candidates. The importance indexes of the input variable candidates can be calculated by solving the linear equation at low computational cost. In this paper, we describe the nonlinear system as an Adaptive Network Based Fuzzy Inference System (ANFIS). Guided by the priori knowledge of input variables, importance indexes and correlation degrees, a fast input variable selection method is presented in which Regularity Criterion is adopted to evaluate the performance of the selected input variables. Experiment results and theoretical analysis both prove that this input variable selection method can speed up the selection process and is more powerful than the typical search tree method.
产权排序: 2
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15033
Appears in Collections:光电信息技术研究室_会议论文

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