New conditions for global stability of neural networks with application to linear and quadratic programming problems

1995 ◽  
Vol 42 (7) ◽  
pp. 354-366 ◽  
Author(s):  
M. Forti ◽  
A. Tesi
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Global Stability ◽  
Quadratic Programming Problems

NOVEL EXPONENTIAL STABILITY CONDITIONS FOR A CLASS OF INTERVAL PROJECTION NEURAL NETWORKS

2009 ◽  
Vol 02 (03) ◽  
pp. 287-297 ◽  
Author(s):  
ZIXIN LIU ◽  
SHU LÜ ◽  
SHOUMING ZHONG
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Convex Quadratic Programming ◽  
Stability Conditions ◽  
Lyapunov Functionals ◽  
Numerical Example ◽  
Grönwall Inequality ◽  
Quadratic Programming Problems

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.

scholarly journals Memristor Neural Networks for Linear and Quadratic Programming Problems

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Mauro Di Marco ◽  
Mauro Forti ◽  
Luca Pancioni ◽  
Giacomo Innocenti ◽  
Alberto Tesi
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Quadratic Programming Problems
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