A one-layer recurrent neural network for convex programming

This paper presents a one-layer recurrent neural network for solving convex programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimization problem. Compared with the existing neural...

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Bibliographic Details
Published in:2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) Vol. 10; pp. 83 - 90
Main Authors: Liu, Qingshan, Wang, Jun
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2008
Subjects:
Artificial neural networks
Computational modeling
Computer architecture
Neurons
Optimization
Programming
Recurrent neural networks
ISBN:1424418208, 9781424418206, 9781424432196, 1424432197
ISSN:2161-4393, 1522-4899
Online Access:Get full text
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Summary:This paper presents a one-layer recurrent neural network for solving convex programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimization problem. Compared with the existing neural networks for optimization, the proposed neural network has lower model complexity. Moreover, the proposed neural network is proved to be globally convergent to the optimal solution(s) under some mild conditions. Simulation results show the effectiveness and performance of the proposed neural network.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISBN:1424418208
9781424418206
9781424432196
1424432197
ISSN:2161-4393
1522-4899
DOI:10.1109/IJCNN.2008.4633771