论文信息 - Nonlinear System Identification Using Radial Basis Function-Based Signal-Dependent ARX Model

Nonlinear System Identification Using Radial Basis Function-Based Signal-Dependent ARX Model

Abstract A smooth nonlinear system identification method without resorting to on-line parameter estimation is presented. Based on the radial basis function, a signal-dependent ARX (RBF-ARX) model is established to describe the nonlinear system dynamics. Especially, a new structured nonlinear parameter optimization algorithm based on the Levenberg-Marquardt algorithm and the least squares method is proposed for estimating the parameters of the nonlinear model.

[1] Jean-Marc Vesin,et al. An amplitude-dependent autoregressive model based on a radial basis functions expansion , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[3] H. Akaike. A new look at the statistical model identification , 1974 .

[4] Zhaoyun Shi,et al. Nonlinear time series modelling with the radial basis function-based state-dependent autoregressive model , 1999, Int. J. Syst. Sci..

[5] D. Marquardt. An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6] T. Johansen,et al. Constructing NARMAX models using ARMAX models , 1993 .

[7] Thomas F. Coleman,et al. Optimization Toolbox User's Guide , 1998 .

[8] Sheng Chen,et al. Non-linear systems identification using radial basis functions , 1990 .

[9] Shang-Liang Chen,et al. Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[10] M. Priestley. STATE‐DEPENDENT MODELS: A GENERAL APPROACH TO NON‐LINEAR TIME SERIES ANALYSIS , 1980 .