B-spline recurrent neural network and its application to modelling of non-linear dynamic systems

Author

Chan, C.W. ; Cheung, K.C. ; Hong Jin ; Zhang, H.Y.

Author_Institution

Dept. of Mech. Eng., Hong Kong Univ., Hong Kong

Volume

1

fYear

1998

fDate

21-26 Jun 1998

Firstpage

78

Abstract

A new recurrent neural network based on B-spline function approximation is presented. The network can be easily trained and its training converges more quickly than that for other recurrent neural networks. Moreover, an adaptive weight updating algorithm for the recurrent network is proposed. It can speed up the training process of the network greatly and its learning speed is more quickly than existing algorithms, e.g., back-propagation algorithm. Examples are presented comparing the adaptive weight updating algorithm and the constant learning rate method, and illustrating its application to modelling of nonlinear dynamic system

Keywords

convergence; function approximation; learning (artificial intelligence); modelling; nonlinear dynamical systems; recurrent neural nets; splines (mathematics); B-spline function approximation; B-spline recurrent neural network; adaptive weight updating algorithm; constant learning rate method; learning speed; network training convergence; nonlinear dynamic system modelling; Adaptive systems; Aerodynamics; Feedback loop; Mechanical engineering; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Recurrent neural networks; Spline;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1998. Proceedings of the 1998

Conference_Location

Philadelphia, PA

ISSN

0743-1619

Print_ISBN

0-7803-4530-4

Type

conf

DOI

10.1109/ACC.1998.694632

Filename

694632