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《Chinese Journal of Sensors and Actuators》 2011-10
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A TVARMA Model Identification Method Based on Modified LS-SVM

WANG Yuegang,DENG Weiqiang,SHAN Bin(Department 304,The Second Artillery Engineering College,Xi ' an 710025,China)  
An improved Least Squares Support Vector Machine(LS-SVM)was proposed and applied to identify the Time-Varying Auto-Regressive Moving-Average(TVARMA)model.Compared with traditional LS-SVM,the Structural Risk Matrix Q and the Empirical Risk weights vi were combined,which reduced the data space and had satisfying flexibility and adaptability.The method was successfully applied to identify the TVARMA model parameters.The experimental results verified its feasibility.
【Fund】: 国家安全重大基础研究项目(973)(613550203)
【CateGory Index】: TP18
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