A high-performance feedback neural network for solving convex nonlinear programming problems

Authors:
Yee Leung;Kai-Zhou Chen;Xing-Bao Gao
Affiliations:
Dept. of Geogr. & Resource Manage., Chinese Univ. of Hong Kong, China;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2003

Citing 0
Cited 8

Novel artificial neural network with simulation aspects for solving linear and quadratic programming problems

Computers & Mathematics with Applications
A new neural network for solving nonlinear projection equations

Neural Networks
A new neural network for solving nonlinear programming problems

ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A dynamical model for solving degenerate quadratic minimax problems with constraints

Journal of Computational and Applied Mathematics
A neural network methodology of quadratic optimization with quadratic equality constraints

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Differential inclusions-based neural networks for nonsmooth convex optimization on a closed convex subset

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Solving general convex nonlinear optimization problems by an efficient neurodynamic model

Engineering Applications of Artificial Intelligence
An application of a merit function for solving convex programming problems

Computers and Industrial Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on a new idea of successive approximation, this paper proposes a high-performance feedback neural network model for solving convex nonlinear programming problems. Differing from existing neural network optimization models, no dual variables, penalty parameters, or Lagrange multipliers are involved in the proposed network. It has the least number of state variables and is very simple in structure. In particular, the proposed network has better asymptotic stability. For an arbitrarily given initial point, the trajectory of the network converges to an optimal solution of the convex nonlinear programming problem under no more than the standard assumptions. In addition, the network can also solve linear programming and convex quadratic programming problems, and the new idea of a feedback network may be used to solve other optimization problems. Feasibility and efficiency are also substantiated by simulation examples.