Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems

Abstract

This paper focuses on the identification problem of Wiener nonlinear systems. The application of the key-term separation principle provides a simplified form of the estimated parameter model. To solve the identification problem of Wiener nonlinear systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the information vector with their corresponding iterative estimates. The simulation results indicate that the proposed algorithm is effective.

Parameter Estimation of Hybrid Fractional-Order Hammerstein-Wiener Box-Jenkins Models Using RIVCF Method

“…The approaches adapted in [11], [12] express the Hammerstein and Wiener models linearly in parameters. The key term separation principle and estimated linear outputs, adopted in [11], [12], are also used in the case of the Wiener model in [13]. The principle drawback of this approach is that the convergence is not guaranteed.…”

Identification of Wiener Time Delay Systems Based on Hierarchical Gradient Approach

“…Therefore, the parameters of this model are linearly related to the output which allows the extension of some results of linear systems to nonlinear ones. Several methods have been proposed in the literature for the identification of Wiener systems Jin et al (2013); Han and Callafon (2012); Zhou et al (2013); Aljamaan et al (2011); Vanbeylen et al (2009);Raich et al (2005); Wang and Ding (2011); Greblicki (1992); Wigren (1993); Figueroa et al (2008); Vörös (2003Vörös ( , 2007. These methods can be classified in different categories of solutions such as the overparametrization solution Han and Callafon (2012), the separable least squares solution Aljamaan et al (2011), the blind solution Vanbeylen et al (2009), the subspace solution Raich et al (2005), the iterative solution Zhou et al (2013); Wang and Ding (2011), and so on.…”

“…These methods can be classified in different categories of solutions such as the overparametrization solutionHan and Callafon (2012), the separable least squares solutionAljamaan et al (2011), the blind solutionVanbeylen et al (2009), the subspace solutionRaich et al (2005), the iterative solutionZhou et al (2013);Wang and Ding (2011), and so on.…”

Twin Support Vector Machine Method for Identification of Wiener Models

“…Common nonlinear models of this type are the Wiener and Hammerstein models [2]. Many algorithms have been proposed to identify Wiener models [3][4][5][6][7][8][9][10][11]. As one might notice from these researches, the extensive knowledge about linear time invariant (LTI) system representations was applied to the dynamic linear blocks.…”