DocumentCode :
2778558
Title :
A discrete-time switching neural network for quadratic programming
Author :
Chen, S. ; Li, S. ; Liang, Y. ; Lou, Y.
Author_Institution :
Key Lab. of Visual Media Process. & Transm., Shenzhen Inst. of Inf. Technol., Shenzhen, China
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
5
Abstract :
This paper presents a discrete-time neural network with a switching structure to solve a general quadratic programming problem in real time. Compared with existing ones for solving quadratic programming problems, the proposed neural network model has a simple architecture and uses a limited number of neurons to solve the problem, irrespective of the dimension of the decision variables or the number of constraints. The global convergence of the model is proven using contraction theory. Simulations are performed to demonstrate the effectiveness of the proposed method.
Keywords :
convergence; mathematics computing; neural nets; quadratic programming; contraction theory; discrete-time switching neural network; global convergence; neuron; quadratic programming; switching structure; Linear matrix inequalities; Programming; Switches; Neural network; contraction theory; global convergence; quadratic programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252844
Filename :
6252844
Link To Document :
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