Title
A novel neural dynamical approach to convex quadratic program and its efficient applications.
Abstract
This paper proposes a novel neural dynamical approach to a class of convex quadratic programming problems where the number of variables is larger than the number of equality constraints. The proposed continuous-time and proposed discrete-time neural dynamical approach are guaranteed to be globally convergent to an optimal solution. Moreover, the number of its neurons is equal to the number of equality constraints. In contrast, the number of neurons in existing neural dynamical methods is at least the number of the variables. Therefore, the proposed neural dynamical approach has a low computational complexity. Compared with conventional numerical optimization methods, the proposed discrete-time neural dynamical approach reduces multiplication operation per iteration and has a large computational step length. Computational examples and two efficient applications to signal processing and robot control further confirm the good performance of the proposed approach.
Year
DOI
Venue
2009
10.1016/j.neunet.2009.03.020
Neural Networks
Keywords
Field
DocType
proposed discrete-time neural dynamical,equality constraint,computational example,proposed continuous-time,neural dynamical method,convex quadratic program,novel neural dynamical approach,neural dynamical approach,proposed neural dynamical approach,low computational complexity,quadratic program,large computational step length,efficient application,discrete time,computational complexity,robot control,signal processing,multiplication operator
Signal processing,Multiplication,Artificial intelligence,Quadratic programming,Artificial neural network,Mathematical optimization,Projected dynamical system,Algorithm,Discrete time and continuous time,Convex optimization,Machine learning,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
22
10
1879-2782
Citations 
PageRank 
References 
13
0.69
24
Authors
2
Name
Order
Citations
PageRank
Youshen Xia11795123.60
Changyin Sun22002157.17