Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals

Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals

This paper considers a neuro-fuzzy based identification problem for Wiener model with controlled autoregressive moving average noise. The separable signal is applied to decouple the dynamic linear part and the static nonlinear part, and the correlation analysis method is adopted to estimate the para...

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Main Authors: Bensheng Lyu, Li Jia, Feng Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Wiener model
separable signal
correlation analysis
stochastic gradient
Online Access:https://ieeexplore.ieee.org/document/9063685/
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spelling doaj-d9a56a3eb7f04ceda58955feb2f1e8462021-03-30T03:16:12ZengIEEEIEEE Access2169-35362020-01-018670476705810.1109/ACCESS.2020.29839699063685Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable SignalsBensheng Lyu0Li Jia1https://orcid.org/0000-0002-5566-9209Feng Li2Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, ChinaDepartment of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, ChinaCollege of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, ChinaThis paper considers a neuro-fuzzy based identification problem for Wiener model with controlled autoregressive moving average noise. The separable signal is applied to decouple the dynamic linear part and the static nonlinear part, and the correlation analysis method is adopted to estimate the parameters of the linear part. To improve the convergence rate of generalized extended stochastic gradient (GESG) algorithm, a generalized extended stochastic gradient algorithm with a forgetting factor is derived for estimating the parameters of the nonlinear part and the parameters of noise model. Examples results verify the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/9063685/Wiener modelseparable signalcorrelation analysisstochastic gradient
collection DOAJ
language English
format Article
sources DOAJ
author Bensheng Lyu
Li Jia
Feng Li
spellingShingle Bensheng Lyu
Li Jia
Feng Li
Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
IEEE Access
Wiener model
separable signal
correlation analysis
stochastic gradient
author_facet Bensheng Lyu
Li Jia
Feng Li
author_sort Bensheng Lyu
title Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
title_short Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
title_full Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
title_fullStr Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
title_full_unstemmed Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals
title_sort parameter estimation of neuro-fuzzy wiener model with colored noise using separable signals
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description This paper considers a neuro-fuzzy based identification problem for Wiener model with controlled autoregressive moving average noise. The separable signal is applied to decouple the dynamic linear part and the static nonlinear part, and the correlation analysis method is adopted to estimate the parameters of the linear part. To improve the convergence rate of generalized extended stochastic gradient (GESG) algorithm, a generalized extended stochastic gradient algorithm with a forgetting factor is derived for estimating the parameters of the nonlinear part and the parameters of noise model. Examples results verify the effectiveness of the proposed method.
topic Wiener model
separable signal
correlation analysis
stochastic gradient
url https://ieeexplore.ieee.org/document/9063685/
work_keys_str_mv AT benshenglyu parameterestimationofneurofuzzywienermodelwithcolorednoiseusingseparablesignals
AT lijia parameterestimationofneurofuzzywienermodelwithcolorednoiseusingseparablesignals
AT fengli parameterestimationofneurofuzzywienermodelwithcolorednoiseusingseparablesignals
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