Robust extended recursive least squares identification algorithm for Hammerstein systems with dynamic disturbances

Dong, Shijian; Zhang, Wen-An; Chen, Bo

doi:10.1016/j.dsp.2020.102716

Cited by 54 publications

(20 citation statements)

References 28 publications

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“…It can be seen from the above formulas that the value of the operator changes with the individual's fitness value score. When the individual's fitness value is low, the score will be high according to equation (13), and the value of F G i and CR G i will decrease. When the individual's fitness value is higher than the average, the score is low, and the value of F G i and CR G i will increase accordingly.…”

Section: Parameters Adaption Mutation Operator and Crossovermentioning

confidence: 99%

“…Due to the wide application of the Hammerstein model, how to identify accurate mathematical models has become the research direction of many researchers. At present, some methods that can effectively identify the Hammerstein model are proposed, such as the least squares algorithm [13], maximum likelihood algorithm [14,15], etc. ere are also some improved algorithms based on traditional algorithms, such as support vector machines [16,17] and multiinnovative stochastic gradient [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm.

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Section: Parameters Adaption Mutation Operator and Crossovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…System identification and model parameter estimation are basic in controller design, dynamic systems modeling, and signal processing 1,2 . Different identification methods have been proposed for linear systems and nonlinear systems, such as the least squares methods, 3‐5 the maximum likelihood methods, 6‐8 the gradient methods, 9 the orthogonal matching pursuit methods, 10 and the robust identification methods 11,12 . However, most of these methods assumed that the input–output data are available at every sampling instant.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 Different identification methods have been proposed for linear systems and nonlinear systems, such as the least squares methods, [3][4][5] the maximum likelihood methods, [6][7][8] the gradient methods, 9 the orthogonal matching pursuit methods, 10 and the robust identification methods. 11,12 However, most of these methods assumed that the input-output data are available at every sampling instant. In other words, the outputs and the inputs have the same sampling rates.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

2020

Adaptive Control & Signal

173

104

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For a dual-rate sampled-data stochastic system with additive colored noise, a dual-rate identification model is obtained by using the polynomial transformation technique, which is suitable for the available dual-rate measurement data. Based on the obtained model, a maximum likelihood least squares-based iterative (ML-LSI) algorithm is presented for identifying the parameters of the dual-rate sampled-data stochastic system. In order to improve the computation efficiency of the algorithm, the identification model of a dual-rate sampled-data stochastic system is divided into two subidentification models with smaller dimensions and fewer parameters, and a maximum likelihood hierarchical least squares-based iterative (H-ML-LSI) algorithm is proposed for these subidentification models by using the hierarchical identification principle. The simulation results indicate that the proposed algorithms are effective for identifying dual-rate sampled-data stochastic systems and the H-ML-LSI algorithm has a higher computation efficiency than the ML-LSI algorithm.

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“…Consequently, the research of nonlinear systems has attracted numerous attention 2‐4 . For the Hammerstein nonlinear systems with dynamic disturbances and measurement noise, Dong et al proposed an extended recursive least squares algorithm based on the auxiliary model identification idea 5 . On the parameter estimation of Hammerstein output‐error systems, Li et al presented a maximum likelihood Levenberg‐Marquardt recursive algorithm by making sufficient use of the interval‐varying input‐output data 6 …”

Section: Introductionmentioning

confidence: 99%

Recursive least squares estimation methods for a class of nonlinear systems based on non‐uniform sampling

Liu

Chen

Ding

et al. 2021

Adaptive Control & Signal

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Summary Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein‐Wiener nonlinear systems based on non‐uniform sampling. By means of the auxiliary model identification idea, an auxiliary model‐based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model‐based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.

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Robust extended recursive least squares identification algorithm for Hammerstein systems with dynamic disturbances

Cited by 54 publications

References 28 publications

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

Recursive least squares estimation methods for a class of nonlinear systems based on non‐uniform sampling

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