NDLI: A Novel Recurrent Generalized Congruence Neural Network for Dynamical System Identification

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A Novel Recurrent Generalized Congruence Neural Network for Dynamical System Identification

Content Provider	IEEE Xplore Digital Library
Author	Tianyun Yan Hefei Ling Shurong Zou
Copyright Year	2007
Description	Author affiliation: ChengDu Univ. of Inf. Technol., Chengdu (Tianyun Yan)
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.
Starting Page	39
Ending Page	43
File Size	244175
Page Count	5
File Format	PDF
ISBN	9780769528755
DOI	10.1109/ICNC.2007.122
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Delay systems Neural networks Neurons Artificial neural networks System identification Nonlinear dynamical systems Information technology Arithmetic Convergence
Content Type	Text
Resource Type	Article

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