Volume 36 Issue 10
Oct.  2010
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He Liqing, Sun Xianfang, Qiu Hongzhuanet al. Structure selection and parameter set-membership identification for nonlinear systems[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(10): 1189-1193. (in Chinese)
Citation: He Liqing, Sun Xianfang, Qiu Hongzhuanet al. Structure selection and parameter set-membership identification for nonlinear systems[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(10): 1189-1193. (in Chinese)

Structure selection and parameter set-membership identification for nonlinear systems

  • Received Date: 31 Aug 2009
  • Publish Date: 31 Oct 2010
  • Based on support vector regression and radial basis function (RBF) neural network, the set-membership identification for nonlinear systems with unknown-but-bounded noise was investigated. The relationships among the ε-insensitive parameter, noise bounds and the number of support vectors were deduced, and the method of determining the ε-insensitive parameter using the noise bounds was introduced. The algorithm of choosing the scale of RBF neural network via support vector regression was described, in which the Gaussian kernel function was taken as the radial basis function and the support vector as its center parameters. After the structure of the RBF neural network was determined, all the feasible weight vectors of the RBF neural network were found by the optimal bounding ellipsoid (OBE) algorithm and a class of feasible nonlinear models were formed which were consistent with the given noise bound series and the input-output data set. A simulation example shows that the proposed algorithm is effective.

     

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  • [1] Huang Y F.A recursive estimation algorithm using selective updating for spectral analysis and adaptive signal processing [J].IEEE Transaction on Acoustics, Speech, and Signal Processing, 1986, 34 (5): 1331-1334 [2] Belforte G, Bona B.An improved parameter identification algorithm for signals with unknown-but-bounded errors // Barker H A, Young P C.7th IFAC/IFORS Symposium on Identification and System Parameter Estimation.York: Pergamon Press, 1985: 1507-1512 [3] Deller J R Jr, Gollamudi S, Nagaraj S, et al.Convergence analysis of the quasi-OBE algorithm and related performance issues[J].International Journal of Adaptive Control and Signal Processing, 2007, 21(6):499 -527 [4] Chisci L, Garulli A,Zappa G.Recursive state bounding by parallelotopes[J].Automatica, 1996, 32(7):1049-1055 [5] Sun X F, Fan Y Z.Guaranteed sensor fault detection and isolation via recursive rectangular parallelepiped bounding in state-set estimation //Proc 3rd ASCC.Shanghai: , 2000: 3041-3046 [6] Alamo T, Bravo J M,Camacho E F.Guaranteed state estimation by zonotopes[J].Automatica, 2005, 41(6):1035-1043 [7] Walter E,Piet-lahanier H.Exact recursive polyhedral description of the feasible parameter set for bounded-error models[J].IEEE Transactions on Automatic Control, 1989, 34(8): 911-915 [8] Kieffer M,Walter E.Interval analysis for guaranteed non-linear parameter and state estimation[J].Mathematical and Computer Modelling of Dynamical Systems, 2005, 11(2): 171-181 [9] Alamo T, Bravo J M, Redondo M J, et al.A set-membership state estimation algorithm based on DC functions [J].Automatica, 2008, 44(1):216-224 [10] Keesman K J,Stappers R.Nonlinear set-membership estimation: a support vector machine approach[J].Journal of Inverse and Ill-posed Problem, 2004, 12(1): 27-41 [11] Scholte E,Campbell M E.A nonlinear set-membership filter for on-line application[J].International Journal of Robust and Nonlinear Control,2003, 13(15):1337-1358 [12] Cristianini N, Shawe-Taylor J.An introduction to support vector machines and other kernel-based learning methods[M].New York:Cambridge University Press, 2000: 30-32, 114-118
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