Identification of singularly perturbed nonlinear system using recurrent high-order neural network

Dongdong Zheng; Wen Fang Xie; Shu Ling Dai

doi:10.1109/WCICA.2014.7053707

Identification of singularly perturbed nonlinear system using recurrent high-order neural network

Dongdong Zheng, Wen Fang Xie, Shu Ling Dai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer neural network [10]. An on-line identification scheme - optimal bounded ellipsoid (OBE) algorithm is developed for the recurrent high order neural network (RHONN) model. By adaptively changing the learning rate, the on-line identification scheme can achieve faster convergence compared to the other widely used learning schemes, such as backpropagation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5779-5784
Number of pages	6
Edition	March
ISBN (Electronic)	9781479958252
DOIs	https://doi.org/10.1109/WCICA.2014.7053707
Publication status	Published - 2 Mar 2015
Externally published	Yes
Event	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Number	March
Volume	2015-March

Conference

Conference	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Keywords

Multi time-scale system
Optimal bounded ellipsoid
Recurrent high order neural network

Access to Document

10.1109/WCICA.2014.7053707

Cite this

Zheng, D., Xie, W. F., & Dai, S. L. (2015). Identification of singularly perturbed nonlinear system using recurrent high-order neural network. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014 (March ed., pp. 5779-5784). Article 7053707 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCICA.2014.7053707

Zheng, Dongdong ; Xie, Wen Fang ; Dai, Shu Ling. / Identification of singularly perturbed nonlinear system using recurrent high-order neural network. Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5779-5784 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March).

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title = "Identification of singularly perturbed nonlinear system using recurrent high-order neural network",

abstract = "In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer neural network [10]. An on-line identification scheme - optimal bounded ellipsoid (OBE) algorithm is developed for the recurrent high order neural network (RHONN) model. By adaptively changing the learning rate, the on-line identification scheme can achieve faster convergence compared to the other widely used learning schemes, such as backpropagation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.",

keywords = "Multi time-scale system, Optimal bounded ellipsoid, Recurrent high order neural network",

author = "Dongdong Zheng and Xie, {Wen Fang} and Dai, {Shu Ling}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

year = "2015",

month = mar,

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doi = "10.1109/WCICA.2014.7053707",

language = "English",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

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Zheng, D, Xie, WF & Dai, SL 2015, Identification of singularly perturbed nonlinear system using recurrent high-order neural network. in Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March edn, 7053707, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), no. March, vol. 2015-March, Institute of Electrical and Electronics Engineers Inc., pp. 5779-5784, 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053707

Identification of singularly perturbed nonlinear system using recurrent high-order neural network. / Zheng, Dongdong; Xie, Wen Fang; Dai, Shu Ling.
Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5779-5784 7053707 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identification of singularly perturbed nonlinear system using recurrent high-order neural network

AU - Zheng, Dongdong

AU - Xie, Wen Fang

AU - Dai, Shu Ling

PY - 2015/3/2

Y1 - 2015/3/2

N2 - In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer neural network [10]. An on-line identification scheme - optimal bounded ellipsoid (OBE) algorithm is developed for the recurrent high order neural network (RHONN) model. By adaptively changing the learning rate, the on-line identification scheme can achieve faster convergence compared to the other widely used learning schemes, such as backpropagation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

AB - In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer neural network [10]. An on-line identification scheme - optimal bounded ellipsoid (OBE) algorithm is developed for the recurrent high order neural network (RHONN) model. By adaptively changing the learning rate, the on-line identification scheme can achieve faster convergence compared to the other widely used learning schemes, such as backpropagation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

KW - Multi time-scale system

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Zheng D, Xie WF, Dai SL. Identification of singularly perturbed nonlinear system using recurrent high-order neural network. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5779-5784. 7053707. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March). doi: 10.1109/WCICA.2014.7053707

Identification of singularly perturbed nonlinear system using recurrent high-order neural network

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