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A Model-Free Method for Structual Change Detection Multivariate Nonlinear Time Series 被引量:2

A Model-Free Method for Structual Change Detection Multivariate Nonlinear Time Series
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摘要 In this paper, we apply the recursive genetic programming (RGP) approach to the cognition of a system, and then proceed to the detecting procedure for structural changes in the system whose components are of long memory. This approach is adaptive and model-free, which can simulate the individual activities of the system's participants, therefore, it has strong ability to recognize the operating mechanism of the system. Based on the previous cognition about the system, a testing statistic is developed for the detection of structural changes in the system. Furthermore, an example is presented to illustrate the validity and practical value of the proposed. In this paper, we apply the recursive genetic programming (RGP) approach to the cognition of a system, and then proceed to the detecting procedure for structural changes in the system whose components are of long memory. This approach is adaptive and model-free, which can simulate the individual activities of the system's participants, therefore, it has strong ability to recognize the operating mechanism of the system. Based on the previous cognition about the system, a testing statistic is developed for the detection of structural changes in the system. Furthermore, an example is presented to illustrate the validity and practical value of the proposed.
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2003年第2期36-46,共11页 系统工程与电子技术(英文版)
基金 ThisprojectwassupportedbytheNationalNaturalScienceFoundationofChina (70 1710 0 1)
关键词 Structural changes Recursive genetic programming Model-free method. Structural changes, Recursive genetic programming, Model-free method.
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