NDLI: Two New Types of Zhang Neural Networks Solving Systems of Time-Varying Nonlinear Inequalities

Content Provider	IEEE Xplore Digital Library
Author	Lin Xiao Yunong Zhang
Copyright Year	2004
Abstract	To solve systems of time-varying nonlinear inequalities, this paper proposes two new types of Zhang neural networks. The first new type of Zhang neural network is based on the conventional Zhang 's neural-dynamics design method, and is termed a conventional Zhang neural network (CZNN). The other one is based on a novel variant of the conventional Zhang 's neural-dynamics design method, and is termed a modified Zhang neural network (MZNN). The theoretical analysis of both CZNN and MZNN is presented to show their excellent convergence performance. Compared with CZNN for solving systems of time-varying nonlinear inequalities, it is discovered that MZNN incorporates CZNN as its special case [i.e., MZNN using linear activation functions (MZNNL) reduces to CZNN exactly]. Besides, MZNN using power-sum activation functions (MZNNP) possesses superior convergence performance to CZNN. Computer-simulation results further demonstrate and substantiate the theoretical analysis and efficacy of both CZNN and MZNN for solving systems of time-varying nonlinear inequalities.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	2363
Ending Page	2373
Page Count	11
File Size	2204559
File Format	PDF
ISSN	15498328
Volume Number	59
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time varying systems Convergence Design methodology Recurrent neural networks Vectors Analytical models Zhang neural network (ZNN) Convergence analysis power-sum activation function recurrent neural network (RNN) systems of time-varying nonlinear inequalities
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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