DocumentCode
1582849
Title
A Novel Recurrent Generalized Congruence Neural Network for Dynamical System Identification
Author
Yan, Tianyun ; Ling, Hefei ; Zou, Shurong
Author_Institution
ChengDu Univ. of Inf. Technol., Chengdu
Volume
1
fYear
2007
Firstpage
39
Lastpage
43
Abstract
A novel recurrent generalized congruence neural network (RGCNN) is presented. Compared with traditional recurrent neural networks (RNNs), RGCNN has the following advantages: simple structure (4 layers), no time-consuming iterative derivative operations in updating weights, and fast convergence induced by modulo arithmetic of the generalized congruence neuron. Computer simulations on benchmark examples of dynamical system identification have successfully validated the performance of the proposed RGCNN.
Keywords
identification; recurrent neural nets; convergence; dynamical system identification; generalized congruence neural network; modulo arithmetic; Arithmetic; Artificial neural networks; Convergence; Delay systems; Information technology; Neural networks; Neurons; Nonlinear dynamical systems; Recurrent neural networks; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location
Haikou
Print_ISBN
978-0-7695-2875-5
Type
conf
DOI
10.1109/ICNC.2007.122
Filename
4344150
Link To Document