Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm
Joint Authors
Pan, Tianhong
Guo, Wei
Song, Ying
Yin, Fujia
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-09
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Pan, Tianhong& Guo, Wei& Song, Ying& Yin, Fujia. 2020. Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm. Complexity،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1145449
Modern Language Association (MLA)
Pan, Tianhong…[et al.]. Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm. Complexity No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1145449
American Medical Association (AMA)
Pan, Tianhong& Guo, Wei& Song, Ying& Yin, Fujia. Identification of Wiener Model with Internal Noise Using a Cubic Spline Approximation-Bayesian Composite Quantile Regression Algorithm. Complexity. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1145449
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1145449