DocumentCode :
2020415
Title :
A New Delayed Projection Neural Network for Solving Linear Variational Inequalities and Quadratic Optimization Problems
Author :
Liu, Zixin ; Shu Lu ; Zhong, Shouming
Author_Institution :
Sch. of Appl. Math., Univ. of Electron. Sci. & Technol. of China, Chengdu
Volume :
1
fYear :
2008
fDate :
17-18 Oct. 2008
Firstpage :
211
Lastpage :
214
Abstract :
For solving linear variational inequalities(LVIs) and quadratic optimization problems(QOPs), a new delayed projection neural network is proposed in this paper. And some sufficient conditions ensuring exponential stability are obtained via constructing appropriate Lyapunov functionals. As a special case, a matrix constraint is considered too. In this case, by dividing the network state variables into subgroups according to the character of the activation functions, some more compact sufficient conditions ensuring exponential stability are obtained, and these conditions are only relate to some blocks of the interconnection matrix. One numerical example will be presented, to show the effectiveness of the main results.
Keywords :
asymptotic stability; delays; linear matrix inequalities; mathematics computing; neural nets; quadratic programming; variational techniques; Lyapunov functionals; delayed projection neural network; exponential stability; linear variational inequalities; matrix constraint; quadratic optimization problem; Cellular neural networks; Circuits; Hopfield neural networks; Linear matrix inequalities; Linear programming; Mathematics; Neural networks; Stability; Sufficient conditions; Symmetric matrices; Exponential stability; Projection neural network; Variational inequalities;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design, 2008. ISCID '08. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3311-7
Type :
conf
DOI :
10.1109/ISCID.2008.15
Filename :
4725593
Link To Document :
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