Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

Mohammad Abu Jami'in; Imam Sutrisno; Jinglu Hu

doi:10.1109/ICSMC.2012.6378275

Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

Mohammad Abu Jami'in^*, Imam Sutrisno, Jinglu Hu

^*Corresponding author for this work

Graduate School of Information, Production and Systems

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

10 Citations (Scopus)

Abstract

In this note, we present the modeling of nonlinear dynamical systems with Quasi-ARX neural network using Lyapunov algorithm in learning process. This work exploits the idea on learning algorithm in nonlinear kernel part of Quasi-ARX model to improve stability and fast convergence of error. The proposed algorithm is then employed to model and predict a classical nonlinear system with input dead zone and nonlinear dynamic systems, exhibiting the effectiveness of proposed algorithm. Based on the result of simulation, the proposed algorithm can make the error in process learning become fast convergence, ultimately bounded, and the error distributed uniformly.

Original language	English
Title of host publication	Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages	3147-3152
Number of pages	6
DOIs	https://doi.org/10.1109/ICSMC.2012.6378275
Publication status	Published - 2012 Dec 1
Event	2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of Duration: 2012 Oct 14 → 2012 Oct 17

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Other

Other	2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/Territory	Korea, Republic of
City	Seoul
Period	12/10/14 → 12/10/17

Keywords

Lyapunov
Neural network
Quasi-ARX model
modeling
nonlinear
stability

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/ICSMC.2012.6378275

Cite this

Jami'in, M. A., Sutrisno, I., & Hu, J. (2012). Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. In Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 (pp. 3147-3152). [6378275] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2012.6378275

Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. / Jami'in, Mohammad Abu; Sutrisno, Imam; Hu, Jinglu.

Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012. 2012. p. 3147-3152 6378275 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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

Jami'in, MA, Sutrisno, I & Hu, J 2012, Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. in Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012., 6378275, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 3147-3152, 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, Seoul, Korea, Republic of, 12/10/14. https://doi.org/10.1109/ICSMC.2012.6378275

Jami'in MA, Sutrisno I, Hu J. Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. In Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012. 2012. p. 3147-3152. 6378275. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2012.6378275

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abstract = "In this note, we present the modeling of nonlinear dynamical systems with Quasi-ARX neural network using Lyapunov algorithm in learning process. This work exploits the idea on learning algorithm in nonlinear kernel part of Quasi-ARX model to improve stability and fast convergence of error. The proposed algorithm is then employed to model and predict a classical nonlinear system with input dead zone and nonlinear dynamic systems, exhibiting the effectiveness of proposed algorithm. Based on the result of simulation, the proposed algorithm can make the error in process learning become fast convergence, ultimately bounded, and the error distributed uniformly.",

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AB - In this note, we present the modeling of nonlinear dynamical systems with Quasi-ARX neural network using Lyapunov algorithm in learning process. This work exploits the idea on learning algorithm in nonlinear kernel part of Quasi-ARX model to improve stability and fast convergence of error. The proposed algorithm is then employed to model and predict a classical nonlinear system with input dead zone and nonlinear dynamic systems, exhibiting the effectiveness of proposed algorithm. Based on the result of simulation, the proposed algorithm can make the error in process learning become fast convergence, ultimately bounded, and the error distributed uniformly.

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Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

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