A class of finite-time dual neural networks for solving quadratic programming problems and its -winners-take-all application

2013 ◽  
Vol 39 ◽  
pp. 27-39 ◽  
Author(s):  
Shuai Li ◽  
Yangming Li ◽  
Zheng Wang
2009 ◽  
Vol 02 (03) ◽  
pp. 287-297 ◽  
Author(s):  
ZIXIN LIU ◽  
SHU LÜ ◽  
SHOUMING ZHONG

In this paper, a class of interval projection neural networks for solving quadratic programming problems are investigated. By using Gronwall inequality and constructing appropriate Lyapunov functionals, several novel conditions are derived to guarantee the exponential stability of the equilibrium point. Compared with previous results, the conclusions obtained here are suitable not only to convex quadratic programming problems but also to degenerate quadratic programming problems, and the conditions are more weaker than the earlier results reported in the literature. In addition, one numerical example is discussed to illustrate the validity of the main results.


2020 ◽  
pp. 1-14 ◽  
Author(s):  
Mauro Di Marco ◽  
Mauro Forti ◽  
Luca Pancioni ◽  
Giacomo Innocenti ◽  
Alberto Tesi

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meng Hui ◽  
Jiahuang Zhang ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Rui Yao ◽  
...  

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