Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

Ma, Junxia; Ding, Feng; Yang, Erfu

doi:10.1007/s11071-015-2454-x

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Note that the unmeasurable terms x i (t − j) are replaced with their auxiliary model output estimatesx i (t − j). Then, the parameter vector θ can be estimated by the auxiliary model based least squares iterative (AM-LSI) algorithm [28],…”

Section: Identification Algorithmmentioning

confidence: 99%

Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model

You

Liu

2018

Algorithms

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This paper focuses on the joint estimation of parameters and time-delays of the multiple-input single-output output-error systems. Since the time-delays are unknown, an effective identification model with a high dimensional and sparse parameter vector is established based on overparameterization. Then, the identification problem is converted to a sparse optimization problem. Based on the basis pursuit de-noising criterion and the auxiliary model identification idea, an auxiliary model based basis pursuit de-noising iterative algorithm is presented. The parameters are estimated by solving a quadratic program, and the unavailable terms in the information vector are updated by the auxiliary model outputs iteratively. The time-delays are estimated according to the sparse structure of the parameter vector. The proposed method can obtain effective estimates of the parameters and time-delays from few sampled data. The simulation results illustrate the effectiveness of the proposed algorithm.

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Section: Identification Algorithmmentioning

confidence: 99%

Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model

You

Liu

2018

Algorithms

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“…This type of nonlinear models are widely existed in modern society, for example, in life science, in chemical engineering and in economic systems [2,3]. Compared with the polynomial nonlinear model, the rational model provides a very concise and parsimonious representation for complex nonlinear systems and has excellent extrapolation properties [4,5,6]. Though the rational model has many advantages over the polynomial model and is widely used in many fields, surprisingly, there is only scattered work reported in the literature on identification of rational models.…”

Section: Introductionmentioning

confidence: 99%

Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

Chen

Zhu

et al. 2017

Nonlinear Dyn

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This paper develops a biased compensation recursive least squares based threshold (BCRLS-TH) algorithm for a time-delay rational model. The time-delay rational model is first transformed into an augmented model by using the redundant rule, and then a RLS algorithm is proposed to estimate the parameters of the augmented model. Since the output of the augmented model is correlated with the noise, a biased compensation method is derived to eliminate the bias of the parameter estimates. Furthermore, based on the structures of the augmented model parameter vector and the rational model parameter vector, the unknown time-delay can be computed by using a threshold given in prior. A simulated example is used to illustrate the efficiency of the proposed algorithm.

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“…The so-called Hammerstein model consists of a static nonlinear block followed by a linear dynamic block and is supposed to represent actuator nonlinearities. The identification of nonlinear dynamic systems using the Hammerstein model with different types of input nonlinearities has been an active research area for many years and some recent works dealing with the Hammerstein models can be found eg in [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Identification of Nonlinear Cascade Systems with Noninvertible Piecewise Linear Input and Backlash Output Nonlinearities

Vörös

2016

Journal of Electrical Engineering

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The paper deals with the parameter identification of cascade nonlinear dynamic systems with noninvertible piecewise linear input nonlinearities and backlash output nonlinearities. Application of the key term separation principle provides special expressions for the corresponding nonlinear model description that are linear in parameters. A least squares based iterative technique allows estimation of all the model parameters based on measured input/output data. Simulation studies illustrate the feasibility of proposed identification method.K e y w o r d s: nonlinear systems, parameter estimation, least-squares, piecewise linear function, backlash

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Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

Cited by 8 publications

References 46 publications

Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model

Iterative Identification for Multivariable Systems with Time-Delays Based on Basis Pursuit De-Noising and Auxiliary Model

Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

Identification of Nonlinear Cascade Systems with Noninvertible Piecewise Linear Input and Backlash Output Nonlinearities

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