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《Acta Simulata Systematica Sinica》 2003-01
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Support Vector Machine Based Nonlinear Systems Identification

ZHANG Hao-ran, HAN Zheng-zhi, LI Chang-gang (Department of Automation, Shanghai Jiao Tong University, Shanghai 200030, China)  
Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. This paper uses support vector machine to model nonlinear dynamical systems, and briefly describes the theory of v-SVR. A simulation example is taken to demonstrate correctness and effectiveness of the proposed approach.
【CateGory Index】: TP183
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