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

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

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 ap...

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Main Authors: Tianhong Pan, Wei Guo, Ying Song, Fujia Yin
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/9195819
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spelling doaj-1dffc57a746146dcab0453344451afee2020-11-25T02:06:38ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/91958199195819Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression AlgorithmTianhong Pan0Wei Guo1Ying Song2Fujia Yin3Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, Anhui, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, Anhui, ChinaSchool of Electrical Information and Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, ChinaSchool of Electrical Information and Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, ChinaA 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.http://dx.doi.org/10.1155/2020/9195819
collection DOAJ
language English
format Article
sources DOAJ
author Tianhong Pan
Wei Guo
Ying Song
Fujia Yin
spellingShingle Tianhong Pan
Wei Guo
Ying Song
Fujia Yin
Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
Complexity
author_facet Tianhong Pan
Wei Guo
Ying Song
Fujia Yin
author_sort Tianhong Pan
title Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
title_short Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
title_full Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
title_fullStr Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
title_full_unstemmed Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
title_sort identification of wiener model with internal noise using a cubic spline approximation-bayesian composite quantile regression algorithm
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description 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.
url http://dx.doi.org/10.1155/2020/9195819
work_keys_str_mv AT tianhongpan identificationofwienermodelwithinternalnoiseusingacubicsplineapproximationbayesiancompositequantileregressionalgorithm
AT weiguo identificationofwienermodelwithinternalnoiseusingacubicsplineapproximationbayesiancompositequantileregressionalgorithm
AT yingsong identificationofwienermodelwithinternalnoiseusingacubicsplineapproximationbayesiancompositequantileregressionalgorithm
AT fujiayin identificationofwienermodelwithinternalnoiseusingacubicsplineapproximationbayesiancompositequantileregressionalgorithm
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