Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method

Wang, Dongqing; Li, Liwei; Ji, Yan; Yan, Yaru

doi:10.1016/j.apm.2017.10.005

Cited by 110 publications

(53 citation statements)

References 44 publications

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“…• The simulation results indicate that the proposed algorithms are effective for estimating the parameters of stochastic systems. • The proposed methods in this paper can be extended to model industrial processes and network systems [79][80][81][82][83][84] by means of some other mathematical tools and approaches [85][86][87][88][89][90].…”

Section: Discussionmentioning

confidence: 99%

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

et al. 2019

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It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

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Section: Discussionmentioning

confidence: 99%

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

et al. 2019

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“…Ding et al presented a hierarchical multi‐innovation gradient estimator for SISO Hammerstein ARMAX models. Furthermore, for Hammerstein‐Wiener and Wiener‐Hammerstein models, various works have been developed and published in the literature dealing with the formulation of problems related to parametric estimation methods . In , a hierarchical least squares algorithm is proposed for the Hammerstein‐Wiener system by based on the auxiliary model identification idea.…”

Section: Introductionmentioning

confidence: 99%

A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

Ghanmi

Elloumi

Salhi

et al. 2019

Asian Journal of Control

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In this paper, a recursive hierarchical parametric estimation (RHPE) algorithm is proposed for stochastic nonlinear systems which can be described by Wiener-Hammerstein (W-H) mathematical models. The formulation of parameters estimation problem is based on the prediction error approach and the gradient techniques. The convergence analysis of the developed RHPE algorithm is derived using stochastic gradient-based theory. Wiener-Hammerstein hydraulic process is treated to prove the efficiency of the proposed approach.

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“…us, blockoriented models have gained wide attention in the system identi cation and automatic control elds [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis Algorithm‐Based Multiple‐Input Single‐Output Wiener Model with Output Noise

Jia

2019

Complexity

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A novel identification algorithm for the correlation analysis-based multiple-input single-output (MISO) neurofuzzy Wiener model with noise is proposed. Firstly, several sets of Gaussian signals are utilized to realize the decoupling between the dynamic linear blocks and the static nonlinear blocks of a MISO Wiener system. Then, the correlation analysis is adopted to identify the parameters of the linear parts, and the problem that the output of static nonlinear block is immeasurable can be solved. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative approach used for the identification of the Wiener model. Next, the least-squares algorithm is employed to estimate the consequent parameters of the nonlinear blocks which can avoid the influence of noise. Finally, examples are used to illustrate the performance of the proposed method.

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Model recovery for Hammerstein systems using the auxiliary model based orthogonal matching pursuit method

Cited by 110 publications

References 44 publications

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

Correlation Analysis Algorithm‐Based Multiple‐Input Single‐Output Wiener Model with Output Noise

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