Analysis and Application of A One-Layer Neural Network for Solving Horizontal Linear Complementarity Problems
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- Horizontal linear complementarity problem, neural network, stability, application
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.
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- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
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 - https://doi.org/10.1080/18756891.2013.858903 ID - Gao2014 ER -