A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

Li, Junhong; Zheng, Wei Xing; Gu, Juping; Hua, Liu

doi:10.1007/s00034-017-0682-7

Cited by 47 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation results show that the proposed algorithms can generate accurate estimates. The proposed approaches in the paper can combine other mathematical tools [64][65][66][67][68][69] and statistical strategies [70][71][72][73][74][75] to study the performances of some parameter estimation algorithms and can be applied to other multivariable systems with different structures and disturbance noises and other literature [76][77][78][79][80][81][82][83][84][85][86] such as system identification [87][88][89][90][91][92].…”

Section: Discussionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Almost all practical industrial processes have nonlinear characteristics to some extent. 1,2 For decades, many methodologies have been carried out in the field of nonlinear dynamic system modeling and identification, for example, Volterra series, 3 block-oriented nonlinear models, [4][5][6][7][8][9][10][11][12] neural networks, 13,14 support vector machines, 15,16 and Markov jump systems. 17 Among these methodologies, block-oriented nonlinear models have received widespread attention due to their simple structure and excellent modeling ability.…”

Section: Introductionmentioning

confidence: 99%

Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance

Chen

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are implemented to completely separate the parameter learning problem of output nonlinear block, linear block, and input nonlinear block. Meanwhile, the static output nonlinear block parameters can be learned based on input and output data of two sets of separable signals with different sizes. Second is to determine the dynamic linear block parameter using correlation analysis algorithm using one set of separable signal; thus, the process disturbance can be compensated by the calculation of correlation function. The final one is to achieve unbiased estimation of the static input nonlinear block parameters using least squares method according to the input–output data of random signal. Furthermore, with the parameter learning method, the proposed model can achieve less computation complexity and good robustness. The simulation results of two cases are provided to demonstrate the advantage of the proposed modeling and parameter learning method.

show abstract

“…Many identification methods for single-input-single-output systems have been studied. 33,34 However, the identification of multiple-input-multiple-output systems is more complex and difficult. This motivates us to study efficient identification methods for multivariate state-space models.…”

Section: Introductionmentioning

confidence: 99%

The innovation algorithms for multivariable state‐space models

Ding

Xiao

2019

Adaptive Control & Signal

115

View full text Add to dashboard Cite

This paper derives the input-output representation of the dynamical system described by a linear multivariable state-space model and the corresponding multivariate linear regressive model (ie, multivariate equation-error model). A projection identification algorithm, a multivariate stochastic gradient identification algorithm, and a multi-innovation stochastic gradient (MISG) identification algorithm are proposed for multivariate equation-error systems by using the negative gradient search and the multi-innovation identification theory. The convergence analysis of the MISG algorithm indicates that the parameter estimation errors converge to zero under the persistent excitation condition. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.

show abstract

A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

Cited by 47 publications

References 35 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

Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance

The innovation algorithms for multivariable state‐space models

Contact Info

Product

Resources

About