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Reproducing wavelet kernel method in nonlinear system identification

Reproducing wavelet kernel method in nonlinear system identification
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摘要 By combining the wavelet decomposition with kernel method, a practical approach of universal multiscale wavelet kernels constructed in reproducing kernel Hilbert space (RKHS) is discussed, and an identification scheme using wavelet support vector machines (WSVM) estimator is proposed for nordinear dynamic systems. The good approximating properties of wavelet kernel function enhance the generalization ability of the proposed method, and the comparison of some numerical experimental results between the novel approach and some existing methods is encouraging. By combining the wavelet decomposition with kernel method, a practical approach of universal multi-scale wavelet kernels constructed in reproducing kernel Hilbert space (RKHS) is discussed, and an identification scheme using wavelet support vector machines (WSVM) estimator is proposed for nonlinear dynamic systems. The good approximating properties of wavelet kernel function enhance the generalization ability of the proposed method, and the comparison of some numerical experimental results between the novel approach and some existing methods is encouraging.
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第2期248-254,共7页 哈尔滨工业大学学报(英文版)
基金 the National 973 Key Fundamental Research Project of China (Grant No.2002CB312200)
关键词 wavelet kernels support vector machine (SVM) reproducing kernel Hilbert space (RKHS) nonlinear system identification 人工智能 自动推理 子波 非线性系统
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参考文献11

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