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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)214-224
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Online published8 Dec 2015
Publication statusPublished - Feb 2016


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.

Research Area(s)

  • Bi-projection model, constrained quadratic otimization, data fusion, fast convergence, recurrent neural network