Support vector machine and higher‐order cumulants based blind identification for non‐linear Wiener models

Xu, Xiaoping; Wang, Feng; Qian, Fucai

doi:10.1049/iet-spr.2017.0384

Cited by 4 publications

(3 citation statements)

References 52 publications

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“…An appropriate map of problems and their assumptions is presented in Table 1 § . To the best knowledge of the authors, it is not possible to find in the literature formally analyzed To emphasize the innovation of our work, we also refer to a contemporary article, 51 where a similar problem to ours is raised with the difference that it shows a blind identification approach, but the nonlinearity is assumed to be parametric and invertible, and there is no proof of consistency.…”

Section: Introduction 11 Why Wiener System?mentioning

confidence: 99%

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Nonparametric identification of Wiener system with a subclass of wide‐sense cyclostationary excitations

Mzyk,

Maik

2023

Adaptive Control & Signal

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SummaryThe paper identifies a Wiener system, which is excited by a cyclostationary time series. To estimate the first subsystem's linear dynamic impulse response: this proposed algorithm first kernel‐windows the Wiener system's input measurements, then cross‐correlates with the output time series. To identify the second subsystem's static nonlinearity: this proposed algorithm first estimates the unobservable inter‐block internal signal (consistently in the statistical sense), and then kernel‐windows these estimates with the Wiener system output. This estimator provides the unusual capability to identify non‐invertible nonlinearities. This strategy removes any restrictive requirement for a Gaussian random excitation or a sinusoidal deterministic excitation. This paper further proves the estimator's asymptotic consistency and determines the kernel bandwidth for algorithmic convergence. The proposed algorithm's efficacy is verified in the context of two common applications: a servo mechanical system and a telecommunication channel.

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Section: Introduction 11 Why Wiener System?mentioning

confidence: 99%

“…To emphasize the innovation of our work, we also refer to a contemporary article, 51 where a similar problem to ours is raised with the difference that it shows a blind identification approach, but the nonlinearity is assumed to be parametric and invertible, and there is no proof of consistency.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric identification of Wiener system with a subclass of wide‐sense cyclostationary excitations

Mzyk,

Maik

2023

Adaptive Control & Signal

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“…Xu et al introduced a sigmoid function to improve the mutation rate F in the Adaptive Di erential Evolution algorithm [5]. Furthermore, a blind identi cation method for the Wiener model was also investigated [6]. e input signal of the system was given by a cyclostationary signal instead of a Gaussian random signal and the internal variables were recovered only based on the output.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm

et al. 2020

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A cubic spline approximation-Bayesian composite quantile regression algorithm is proposed to estimate parameters and structure of the Wiener model with internal noise. Firstly, an ARX model with a high order is taken to represent the linear block; meanwhile, the nonlinear block (reversibility) is approximated by a cubic spline function. Then, parameters are estimated by using the Bayesian composite quantile regression algorithm. In order to reduce the computational burden, the Markov Chain Monte Carlo algorithm is introduced to calculate the expectation of parameters’ posterior distribution. To determine the structure order, the Final Output Error and the Akaike Information Criterion are used in the nonlinear block and the linear block, respectively. Finally, a numerical simulation and an industrial case verify the effectiveness of the proposed algorithm.

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Parameter learning for Wiener systems with time‐delay state‐space model

Li,

Ding,

2024

Asian Journal of Control

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This paper discusses a novel scheme for learning the Wiener output error nonlinear system with time‐delay state‐space model. In the Wiener system, the dynamic linear block is approximated by time‐delay state‐space model, and the static nonlinear block is established using neural fuzzy network. Combined signals designed including separable signal and random signal are devoted to achieving parameters separation learning of the Wiener system, that is, the two blocks are learned independently. Firstly, using the properties of shift operator and transforming state‐space model with time‐delay into a representation with input and output, then linear dynamic block parameters are learned by the virtue of correlation analysis method in the condition of Gaussian signals. Moreover, a recursive extended least squares estimation is carried out to learn parameters of static nonlinear block and colored noise model under the condition of random signals. The efficiency and accuracy of proposed scheme are confirmed on experiment results of a numerical simulation and a typical practical nonlinear process, and experimental simulation results demonstrate that the learning scheme proposed obtains good learning precision.

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Support vector machine and higher‐order cumulants based blind identification for non‐linear Wiener models

Cited by 4 publications

References 52 publications

Nonparametric identification of Wiener system with a subclass of wide‐sense cyclostationary excitations

Nonparametric identification of Wiener system with a subclass of wide‐sense cyclostationary excitations

Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm

Parameter learning for Wiener systems with time‐delay state‐space model

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