Abstract
This paper presents a self-constructing recurrent neural network (SCRNN) capable of building itself with a compact structure from input-output measurements for identification of chaotic systems. The proposed SCRNN is constituted by a static nonlinear network cascaded with a linear dynamic network. A unified learning algorithm consisting of two mechanisms, a hybrid weight initialization method and a parameter optimization method, has been developed for the structure and parameter identification. With this learning algorithm, the SCRNN is exempted from trial and error in structure initialization as well as parameterization. Computer simulations on discrete-time chaotic systems, including logistic and Henon mappings, validate that the proposed SCRNN is capable of capturing the dynamical behavior of chaotic systems with a compact network size.
| Original language | English |
|---|---|
| Pages (from-to) | 2150-2155 |
| Number of pages | 6 |
| Journal | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 3 |
| Publication status | Published - 2005 Dec 1 |
| Event | IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States Duration: 2005 Oct 10 → 2005 Oct 12 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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Identification of chaotic systems using a self-constructing recurrent neural network. / Chen, Yen Ping; Wang, Jeen-Shing.
In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, 01.12.2005, p. 2150-2155.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Identification of chaotic systems using a self-constructing recurrent neural network
AU - Chen, Yen Ping
AU - Wang, Jeen-Shing
PY - 2005/12/1
Y1 - 2005/12/1
N2 - This paper presents a self-constructing recurrent neural network (SCRNN) capable of building itself with a compact structure from input-output measurements for identification of chaotic systems. The proposed SCRNN is constituted by a static nonlinear network cascaded with a linear dynamic network. A unified learning algorithm consisting of two mechanisms, a hybrid weight initialization method and a parameter optimization method, has been developed for the structure and parameter identification. With this learning algorithm, the SCRNN is exempted from trial and error in structure initialization as well as parameterization. Computer simulations on discrete-time chaotic systems, including logistic and Henon mappings, validate that the proposed SCRNN is capable of capturing the dynamical behavior of chaotic systems with a compact network size.
AB - This paper presents a self-constructing recurrent neural network (SCRNN) capable of building itself with a compact structure from input-output measurements for identification of chaotic systems. The proposed SCRNN is constituted by a static nonlinear network cascaded with a linear dynamic network. A unified learning algorithm consisting of two mechanisms, a hybrid weight initialization method and a parameter optimization method, has been developed for the structure and parameter identification. With this learning algorithm, the SCRNN is exempted from trial and error in structure initialization as well as parameterization. Computer simulations on discrete-time chaotic systems, including logistic and Henon mappings, validate that the proposed SCRNN is capable of capturing the dynamical behavior of chaotic systems with a compact network size.
UR - http://www.scopus.com/inward/record.url?scp=27944436518&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:27944436518
VL - 3
SP - 2150
EP - 2155
JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SN - 1062-922X
ER -