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

Mohammad Abu Jami'in; Imam Sutrisno; Takayuki Furuzuki

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, Takayuki Furuzuki

Graduate School of Information, Production and Systems

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

9 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	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages	3147-3152
Number of pages	6
DOIs	https://doi.org/10.1109/ICSMC.2012.6378275
Publication status	Published - 2012
Event	2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul Duration: 2012 Oct 14 → 2012 Oct 17

Other

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

Fingerprint

Keywords

Lyapunov
modeling
Neural network
nonlinear
Quasi-ARX model
stability

ASJC Scopus subject areas

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

Cite this

Jami'in, M. A., Sutrisno, I., & Furuzuki, T. (2012). Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 3147-3152). [6378275] 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; Furuzuki, Takayuki.

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

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

Jami'in, MA, Sutrisno, I & Furuzuki, T 2012, Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system. in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics., 6378275, pp. 3147-3152, 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012, Seoul, 12/10/14. https://doi.org/10.1109/ICSMC.2012.6378275

@inproceedings{93a972a4321540bf88f5ca02a53bd457,

title = "Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system",

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.",

keywords = "Lyapunov, modeling, Neural network, nonlinear, Quasi-ARX model, stability",

author = "Jami'in, {Mohammad Abu} and Imam Sutrisno and Takayuki Furuzuki",

year = "2012",

doi = "10.1109/ICSMC.2012.6378275",

language = "English",

isbn = "9781467317146",

pages = "3147--3152",

booktitle = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",

note = "2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 ; Conference date: 14-10-2012 Through 17-10-2012",

}

TY - GEN

T1 - Lyapunov learning algorithm for Quasi-ARX neural network to identification of nonlinear dynamical system

AU - Jami'in, Mohammad Abu

AU - Sutrisno, Imam

AU - Furuzuki, Takayuki

PY - 2012

Y1 - 2012

N2 - 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.

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.

KW - Lyapunov

KW - modeling

KW - Neural network

KW - nonlinear

KW - Quasi-ARX model

KW - stability

UR - http://www.scopus.com/inward/record.url?scp=84872422005&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872422005&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2012.6378275

DO - 10.1109/ICSMC.2012.6378275

M3 - Conference contribution

AN - SCOPUS:84872422005

SN - 9781467317146

SP - 3147

EP - 3152

BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012

Y2 - 14 October 2012 through 17 October 2012

ER -

Access to Document

10.1109/ICSMC.2012.6378275