Zhang Neural Network Versus Gradient Neural Network for Solving Time-Varying Linear Inequalities

Author

Xiao, Lin ; Zhang, Yunong

Author_Institution

Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China

Volume

22

Issue

10

fYear

2011

Firstpage

1676

Lastpage

1684

Abstract

By following Zhang design method, a new type of recurrent neural network [i.e., Zhang neural network (ZNN)] is presented, investigated, and analyzed for online solution of time-varying linear inequalities. Theoretical analysis is given on convergence properties of the proposed ZNN model. For comparative purposes, the conventional gradient neural network is developed and exploited for solving online time-varying linear inequalities as well. Computer simulation results further verify and demonstrate the efficacy, novelty, and superiority of such a ZNN model and its method for solving time-varying linear inequalities.

Keywords

recurrent neural nets; Zhang design method; Zhang neural network; gradient neural network; recurrent neural network; time-varying linear inequalities; Analytical models; Computational modeling; Convergence; Linear matrix inequalities; Mathematical model; Recurrent neural networks; Vectors; Convergence analysis; Zhang neural network; gradient neural network; solution set; time-varying linear inequalities; Algorithms; Artificial Intelligence; Computer Simulation; Humans; Linear Models; Neural Networks (Computer); Software; Software Design; Stochastic Processes; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2011.2163318

Filename

5983446