A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems

In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2016-02, Vol.27 (2), p.214-224
Main Authors: Xia, Youshen, Wang, Jun
Format: Article
Language:English
Subjects:
Bi-projection model
Biological neural networks
constrained quadratic optimization
Constraints
Convergence
Data fusion
Data integration
fast convergence
Humans
Learning
Mathematical model
Mathematical models
Neural networks
Neural Networks (Computer)
Optimization
Pattern Recognition, Automated - methods
Problem Solving
Projection
recurrent neural network
Recurrent neural networks
Trajectory
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Summary:In this paper, a bi-projection neural network for solving a class of constrained quadratic optimization problems is proposed. It is proved that the proposed neural network is globally stable in the sense of Lyapunov, and the output trajectory of the proposed neural network will converge globally to an optimal solution. Compared with existing projection neural networks (PNNs), the proposed neural network has a very small model size owing to its bi-projection structure. Furthermore, an application to data fusion shows that the proposed neural network is very effective. Numerical results demonstrate that the proposed neural network is much faster than the existing PNNs.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2500618