Zhang neural network versus gradient neural network for solving time-varying linear inequalities

Lin Xiao; Yunong Zhang

doi:10.1109/TNN.2011.2163318

Zhang neural network versus gradient neural network for solving time-varying linear inequalities

IEEE Trans Neural Netw. 2011 Oct;22(10):1676-84. doi: 10.1109/TNN.2011.2163318. Epub 2011 Aug 15.

Authors

Lin Xiao¹, Yunong Zhang

Affiliation

¹ School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China. xiaolin860728@163.com

PMID: 21846604
DOI: 10.1109/TNN.2011.2163318

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.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Artificial Intelligence*
Computer Simulation / standards
Humans
Linear Models*
Neural Networks, Computer*
Software / standards
Software Design
Stochastic Processes
Time Factors