DocumentCode
3344746
Title
Recurrent neural networks for recursive identification of nonlinear dynamic process
Author
Dong, Jia-Wen ; Qian, Ji-Xin ; Sun, You-Xian
Author_Institution
Inst. of Ind. Process Control, Zhejiang Univ., Hangzhou, China
fYear
1994
fDate
5-9 Dec 1994
Firstpage
794
Lastpage
798
Abstract
In this paper, modified Elman-type recurrent neural networks (1990) were developed to identify the dynamic nonlinear systems with generalised backpropagation recursive algorithm. Analysis shows that introduction of adjustable self-connections of context units provides network ability to model high order input-output mapping, unbiased estimates can be achieved without the need to fit additive noise model. An industrial application example shows its efficiency
Keywords
backpropagation; identification; nonlinear dynamical systems; recurrent neural nets; adjustable self-connections; context units; generalised backpropagation recursive algorithm; nonlinear dynamic process; recurrent neural networks; recursive identification; Context modeling; Electrical equipment industry; Industrial control; Multi-layer neural network; Neural networks; Neurofeedback; Parameter estimation; Process control; Recurrent neural networks; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Technology, 1994., Proceedings of the IEEE International Conference on
Conference_Location
Guangzhou
Print_ISBN
0-7803-1978-8
Type
conf
DOI
10.1109/ICIT.1994.467030
Filename
467030
Link To Document