Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method

Wang, Dongqing; Yan, Yaru; Liu, Yanjun; Ding, Junhang

doi:10.1016/j.cam.2018.06.016

Cited by 84 publications

(60 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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“…Once the structure is determined, parameter estimation is involved [5][6][7]. Recently, a lot of parameter estimation algorithms are proposed, including the least squares (LS) algorithm [8,9], the gradient algorithm [10,11], the particle swarm optimization algorithm, and the expectation maximization algorithm [12,13]. Among these algorithms, the LS algorithm is to find a vector that is a local minimizer to a function that is a sum of squares; thus, it is the simplest algorithm and is widely used.…”

Section: Introductionmentioning

confidence: 99%

Weight Least Squares Algorithm for Rational Models with Outliers

2020

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A weight least squares algorithm is developed for rational models with outliers in this paper. Different weights are assigned for each cost function, and by calculating the derivatives of these cost functions, the parameter estimates can be estimated. Compared with the traditional least squares algorithm, the proposed algorithm can remove the bad effect caused by the outliers, thus has more accurate parameter estimates. A simulation example is proposed to validate the effectiveness of the proposed algorithm.

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“…Many methods have been used to deal with system identification problems [5][6][7][8]. Minimizing different criterion leads to different identification methods, such as neural network methods, fuzzy logic system identification methods, wavelet network system identification methods and so on.…”

Section: Introductionmentioning

confidence: 99%

Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

et al. 2019

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This paper is concerned with the identification problem for multivariable equation-error systems whose disturbance is an autoregressive moving average process. By means of the hierarchical identification principle and the iterative search, a hierarchical least-squares-based iterative (HLSI) identification algorithm is derived and a least-squares-based iterative (LSI) identification algorithm is given for comparison. Furthermore, a hierarchical multi-innovation least-squares-based iterative (HMILSI) identification algorithm is proposed using the multi-innovation theory. Compared with the LSI algorithm, the HLSI algorithm has smaller computational burden and can give more accurate parameter estimates and the HMILSI algorithm can track time-varying parameters. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.

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Model recovery for Hammerstein systems using the hierarchical orthogonal matching pursuit method

Cited by 84 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

Weight Least Squares Algorithm for Rational Models with Outliers

Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

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