Analysis and Application of A One-Layer Neural Network for Solving Horizontal Linear Complementarity Problems

DOI

10.1080/18756891.2013.858903How to use a DOI?

Keywords

Horizontal linear complementarity problem, neural network, stability, application

Abstract

In this paper, we analyze the stability and convergence of a one-layer neural network proposed by Gao and Wang, which is designed to solve a class of horizontal linear complementarity problems. The globally asymptotical stability and globally exponential stability of this network are proved strictly under mild conditions, respectively. Meanwhile, this network is applied to solve a transportation problem and a class of the absolute equations.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xingbao Gao
AU  - Jing Wang
PY  - 2014
DA  - 2014/08/01
TI  - Analysis and Application of A One-Layer Neural Network for Solving Horizontal Linear Complementarity Problems
JO  - International Journal of Computational Intelligence Systems
SP  - 724
EP  - 732
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.858903
DO  - 10.1080/18756891.2013.858903
ID  - Gao2014
ER  -