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Volume 30 Issue 8
Jan.  2011
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Yu Dong-jun, Zhen Yu-jie, Wu Xiao-jun, Yang Jing-yu . Kernel-SOM Based Nonlinear System Identification and Model Running Convergence Analysis[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1928-1931. doi: 10.3724/SP.J.1146.2007.00010
Citation: Yu Dong-jun, Zhen Yu-jie, Wu Xiao-jun, Yang Jing-yu . Kernel-SOM Based Nonlinear System Identification and Model Running Convergence Analysis[J]. Journal of Electronics & Information Technology, 2008, 30(8): 1928-1931. doi: 10.3724/SP.J.1146.2007.00010

Kernel-SOM Based Nonlinear System Identification and Model Running Convergence Analysis

doi: 10.3724/SP.J.1146.2007.00010
  • Received Date: 2007-01-05
  • Rev Recd Date: 2007-09-24
  • Publish Date: 2008-08-19
  • A Kernel-SOM based unsupervised nonlinear system identification algorithm is proposed. Analysis of the model running convergence of the proposed algorithm is performed, and the convergence theorem is proofed by considering both identification error and initial input error. Numerical simulation results demonstrate the effectiveness of the proposed identification algorithm and the correctness of the convergence theorem.
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  • Kohonen T. Self-organization map[J].Proc. IEEE.1990, 78(9):1464-1480[2]Barreto G A and Aluizio A F R. Identification and control ofdynamical systems using the self-organizing map[J].IEEETrans. on Neural Networks.2004, 15(5):1244-1259[3]Yu Dong-jun, et al.. Kernel-SOM based visualization offinancial time series forecasting. International Conference oninnovative computing, information and control. Beijing, 2006,Volume II: 470-473.[4]Pan Zhisong, Chen Songcan, and Zhang Daoqiang. AKernel-based SOM classification in input space. ActaElectronica Sinica, 2004, 32(2): 227-231.[5]Scholkopf B, Burges C J C, and Smola A J. Advances inKernel Methods - Support Vector Learning [M]. Cambridge,MA, The MIT Press, 1999: 255-268.[6]Pao Xiaohong, et al.. Model error analysis in nonlinearsystem identification using neural networks (I). Control andDecision, 1997, 12(5): 20-25.[7]Lin C T. Neural Fuzzy Systems. New York: Prentice-HallPress. 1997, Chapter 3.
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