PaperOn nonparametric estimation of nonlinear dynamic systems by the Fourier series estimate
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Cited by (24)
Identification of continuous-time Hammerstein systems by simultaneous perturbation stochastic approximation
2016, Expert Systems with ApplicationsCitation Excerpt :Over the past two decades, various methods for identification of Hammerstein systems have been studied extensively. These can be roughly classified into several categories, such as the iterative method (Liu & Bai, 2007; Narendra & Gallman, 1966; Rangan, Wolodkin, & Polla, 1995; Stoica, 1981; Voros, 1997), the over-parameterization method (Chang & Luus, 1971; Ding, Chen, & Iwai, 2007a; Hsia, 1976), the blind approach (Bai & Fu, 2002), the subspace method (Verhaegen & Westwick, 1996), the least squares method (Ding & Chen, 2005; Goethals, Pelckmans, Suykens, & Moor, 2005), the parametric instrumental variables method (Laurain, Gilson, & Garnier, 2009; Stoica & Soderstrom, 1981), the stochastic method (Bilings & Fakhouri, 1978; Greblicki, 1996; Pawlak, 1991) and the non-parametric identification method (Bai, 2003; Greblicki & Pawlak, 1987; Krzyak, 1993, 1996). Recently, a decomposition-based Newton iterative identification approach for a Hammerstein nonlinear FIR system with ARMA noise was presented by Ding, Deng, and Liu (2014).
Frequency domain analysis and identification of block-oriented nonlinear systems
2011, Journal of Sound and VibrationCitation Excerpt :Therefore, once different orders of output spectrum are achieved, many existing algorithms (e.g., Least Square) can be employed to estimate model parameters involved in each order output spectrum, and finally the nonlinear system model could be obtained. Although frequency domain identification of block-oriented nonlinear systems has been discussed in the literature, existing methods usually assume known structure and invertibility of nonlinearity, or require the linear part to be a rational transfer function, or involve many repeated experiments with different magnitudes and frequencies restrictively using sinusoidal input signals [32,35–38,40]. In this section, to estimate different orders of output spectrum, no priori knowledge is required for model structure and orders.
Computational burden reduction in set-membership Hammerstein system identification
2011, IFAC Proceedings Volumes (IFAC-PapersOnline)Identification of nonlinear systems by nonparametric approach with application to control of flexible robot manipulator
2009, IFAC Proceedings Volumes (IFAC-PapersOnline)On the Hermite series approach to nonparametric identification of Hammerstein systems
2005, IFAC Proceedings Volumes (IFAC-PapersOnline)
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This research was supported by NSERC grant OGP000270, FCAR grant 92-RE-0147, Alexander von Humboldt Fellowship, and by the Vinberg Scholarship, while the author spent sabbatical leave at Computer Science Department at Technion-Israel Institute of Technology. The author gratefully acknowledges the support of the Frontier Research Program RIKEN, Artificial Brain Systems Laboratory, Wakoshi, Japan.