Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle

Chen, Huibo; Xiao, Yongsong; Ding, Feng

doi:10.1016/j.amc.2014.09.070

Cited by 50 publications

(27 citation statements)

References 47 publications

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“…Deng and Ding developed a Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise [40]. Other methods can be referred as to the transfer function identification [41][42][43][44][45], linear system identification [46][47][48][49][50][51], and nonlinear system identification [52][53][54][55][56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input-output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

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Section: Introductionmentioning

confidence: 99%

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Pan

Jiang

et al. 2018

Complexity

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“…The typical nonlinear systems include the Wiener nonlinear system and the Hammerstein nonlinear system, which consist of a linear time-invariant block following (followed by) a memoryless nonlinear block [20][21][22] and the identification problems of nonlinear systems are vibrant [23][24][25]. A large amount of work has been published in this research field [26][27][28]. Recently, Wang [29] presented a filtering and auxiliary model-based recursive least-squares algorithm and a filtering and auxiliary model-based least-squares iterative identification algorithm for Hammerstein nonlinear systems; Zhang [30] derived a recursive leastsquares identification algorithm based on the bias compensation technique for multi-input single-output systems with colored noises; Ding et al [31] proposed a recursive least-squares algorithm for estimating the parameters of the nonlinear systems based on the model decomposition.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical multi-innovation extended stochastic gradient algorithms for input nonlinear multivariable OEMA systems by the key-term separation principle

Shen

Ding

2016

Nonlinear Dyn

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This paper focuses on the identification of input nonlinear multivariable systems (i.e., Hammerstein multi-input multi-output nonlinear systems) described by output-error moving average models. Based on the key-term separation principle, we separate a proper key term in the nonlinear system and transform a complex nonlinear optimization problem into a pseudo-linear optimization problem which does not involve the products of the parameters between the linear parts and the nonlinear parts. Thus, a hierarchical extended stochastic gradient (H-ESG) algorithm is given and a hierarchical multi-innovation extended stochastic gradient (H-MI-ESG) algorithm is derived for the nonlinear systems. The proposed H-MI-ESG algorithm is an extension of the H-ESG algorithms. Compared with the over-parameterization-based leastsquares identification algorithm, the H-ESG and H-MI-ESG algorithms have high computational efficiency. The simulation results show the effectiveness of the proposed algorithms.

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“…In the last few decades, there are many studies on system identification, and 1 the methods in these studies can be roughly divided into the stochastic gradient (SG) algorithm [2][3][4]34], the recursive least squares (RLS) algorithm [12,14,24,30], and the iterative algorithm [10,11,18,20]. The SG algorithm and the RLS algorithm are often used in online estimation, but the iterative algorithm is often used in off-line estimation.…”

Section: Introductionmentioning

confidence: 99%

Identification Methods for Two-Variable Difference Systems

Chen

Jiang

2015

Circuits Syst Signal Process

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This paper proposes two identification methods for two-variable difference systems. The output and the input of the two-variable difference systems depend not only on time but also on spatial coordinates. A recursive least squares algorithm and a stochastic gradient algorithm are introduced to estimate the unknown parameters. Furthermore, in order to increase the convergence rate and to decrease the computational effort, a forgetting factor stochastic gradient algorithm is proposed. The simulation results indicate that the proposed methods are effective.

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Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle

Cited by 50 publications

References 47 publications

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Adaptive Gradient‐Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Hierarchical multi-innovation extended stochastic gradient algorithms for input nonlinear multivariable OEMA systems by the key-term separation principle

Identification Methods for Two-Variable Difference Systems

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