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

• Published in: IEEE transaction on neural networks and learning systems Vol. 27; no. 2; pp. 214 - 224
• Main Authors: Xia, Youshen, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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; data fusion; Bi-projection model; constrained quadratic optimization; recurrent neural network; fast convergence
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2015.2500618

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.