TY - GEN
T1 - Identification for nonlinear singularly perturbed system using recurrent high-order multi-time scales neural network
AU - Zheng, Dongdong
AU - Xie, Wenfang
N1 - Publisher Copyright:
© 2015 American Automatic Control Council.
PY - 2015/7/28
Y1 - 2015/7/28
N2 - A new identification algorithm for nonlinear singularly perturbed system using multi-time scales recurrent highorder neural networks is proposed in this paper. The high-order neural networks have simple structure and strong nonlinear approximation capability, which enables it to model the nonlinear singularly perturbed systems more accurately with less computation complexity, compared to multilayer neural networks. The optimal bounded ellipsoid algorithm, which is originally designed for discrete time systems, is introduced to update the weights of continuous multi-time scales neural networks. Compared to other widely used gradient-like updating methods, the on-line identification algorithm proposed in this paper can realize faster convergence, due to the adaptive 'learning rate' of the weights updating laws. The effectiveness of the proposed scheme is demonstrated by simulation results.
AB - A new identification algorithm for nonlinear singularly perturbed system using multi-time scales recurrent highorder neural networks is proposed in this paper. The high-order neural networks have simple structure and strong nonlinear approximation capability, which enables it to model the nonlinear singularly perturbed systems more accurately with less computation complexity, compared to multilayer neural networks. The optimal bounded ellipsoid algorithm, which is originally designed for discrete time systems, is introduced to update the weights of continuous multi-time scales neural networks. Compared to other widely used gradient-like updating methods, the on-line identification algorithm proposed in this paper can realize faster convergence, due to the adaptive 'learning rate' of the weights updating laws. The effectiveness of the proposed scheme is demonstrated by simulation results.
UR - http://www.scopus.com/inward/record.url?scp=84940941307&partnerID=8YFLogxK
U2 - 10.1109/ACC.2015.7170998
DO - 10.1109/ACC.2015.7170998
M3 - Conference contribution
AN - SCOPUS:84940941307
T3 - Proceedings of the American Control Conference
SP - 1824
EP - 1829
BT - ACC 2015 - 2015 American Control Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 American Control Conference, ACC 2015
Y2 - 1 July 2015 through 3 July 2015
ER -