NDLI: Zhang Neural Network for Linear Time-Varying Equation Solving and its Robotic Application

Content Provider	IEEE Xplore Digital Library
Author	Yu-Nong Zhang Hai-Feng Peng
Copyright Year	2007
Description	Author affiliation: Sun Yat-Sen Univ., Guangzhou (Yu-Nong Zhang)
Abstract	Different from gradient-based neural networks, a special kind of recurrent neural network has been proposed by Zhang et al for real-time matrix inversion. In this paper, we generalize such a design method to solving online a set of linear time-varying equations. In comparison with gradient-based neural networks, the resultant Zhang neural network for time-varying equation solving is designed based on a vector-valued error function, instead of a scalar-valued error function. It is depicted in an implicit dynamics, instead of an explicit dynamics. Furthermore, Zhang neural network globally exponentially converges to the exact solution of linear time-varying equations. Simulation results, including the application to robot kinematic control, substantiate the theoretical analysis and demonstrate the efficacy of Zhang neural network on linear time-varying equation solving, especially when using a power-sigmoid activation function.
Starting Page	3543
Ending Page	3548
File Size	2420800
Page Count	6
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Recurrent neural networks Design methodology Robot control Machine learning Cybernetics Robot kinematics Error correction Differential equations Sun robot kinematic control Time-varying linear equations recurrent neural network error function implicit dynamics power-sigmoid activation function
Content Type	Text
Resource Type	Article

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