Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653577 | Neurocomputing | 2005 | 16 Pages |
Abstract
This paper develops an improved neural network to solve convex quadratic optimization problems with general linear constraints. Compared with the existing primal-dual neural network and dual neural network for solving such problems, the proposed neural network has a lower complexity for implementation. Unlike the Kennedy-Chua neural network, the proposed neural network can converge to an exact optimal solution. Analyzed results and illustrative examples show that the proposed neural network has a fast convergence to the optimal solution. Finally, the proposed neural network is effectively applied to real-time beamforming.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Youshen Xia, Gang Feng,