DocumentCode :
2988120
Title :
Broyden-Method Aided Discrete ZNN Solving the Systems of Time-Varying Nonlinear Equations
Author :
Yunong Zhang ; Chen Peng ; Weibing Li ; Yanyan Shi ; Yingbiao Ling
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
fYear :
2012
fDate :
7-9 Dec. 2012
Firstpage :
492
Lastpage :
495
Abstract :
Recently, a special class of recurrent neural network (termed Zhang neural network, ZNN) has been generalized for solving systems of time-varying nonlinear equations (STVNE), and a resultant continuous (or say, continuous-time) ZNN model has been proposed and analyzed. To generalize the idea for digital computers and numerical algorithms, this paper discretizes the continuous STVNE-solving ZNN using Euler difference and improves the discrete (or say, discrete time) ZNN models by employing Broyden method. Results of various numerical experiments are presented to verify the effectiveness of the proposed discrete ZNN models, especially the Broyden-method aided ones.
Keywords :
mathematics computing; nonlinear equations; recurrent neural nets; Broyden-method aided discrete ZNN; Euler difference; Zhang neural network; continuous STVNE-solving ZNN; discrete-time ZNN model; recurrent neural network; time-varying nonlinear equation; Computational modeling; Convergence; Jacobian matrices; Mathematical model; Nonlinear equations; Numerical models; Time varying systems; Broyden method; Zhang neural network; discrete methods; systems of nonlinear equations; time-varying;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Engineering and Communication Technology (ICCECT), 2012 International Conference on
Conference_Location :
Liaoning
Print_ISBN :
978-1-4673-4499-9
Type :
conf
DOI :
10.1109/ICCECT.2012.84
Filename :
6414057
Link To Document :
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