Asymptotic and Non-asymptotic Confidence Regions in Closed Loop Model Validation
Advances in Wireless Communications and Networks
Volume 3, Issue 6, November 2017, Pages: 75-83
Received: Oct. 5, 2017; Accepted: Nov. 17, 2017; Published: Dec. 5, 2017
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Authors
Hong Wang-Jian, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
Tang De-zhi, School of Electrical and Information Engineering, Anhui University of Technology, Ma-an-shan, China
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Abstract
In this paper one way is proposed to construct asymptotic and non-asymptotic confidence regions in the problem of closed loop model validation deeply. These two asymptotic and non-asymptotic confidence regions correspond to the infinite and finite data points. Firstly one asymptotic confidence region is derived from some statistical properties on noise. The uncertainties bound of the model parameter is constructed in the probability sense by using the inner product form of the asymptotic covariance matrix, then a new technique for estimating bias and variance contributions to the model error is suggested. Secondly we modify sign perturbed sums (SPS) method to construct non-asymptotic confidence regions under a finite number of data points, where some modifications are studied for closed loop system. Finally the simulation example results confirm the identification theoretical results.
Keywords
Closed Loop Identification, Model Structure Validation, Asymptotic Region, Non-asymptotic Region
To cite this article
Hong Wang-Jian, Tang De-zhi, Asymptotic and Non-asymptotic Confidence Regions in Closed Loop Model Validation, Advances in Wireless Communications and Networks. Vol. 3, No. 6, 2017, pp. 75-83. doi: 10.11648/j.awcn.20170306.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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