Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 807656, 18 pages
http://dx.doi.org/10.1155/2012/807656
Research Article

A Recurrent Neural Network for Nonlinear Fractional Programming

1Management Department, City College of Dongguan University of Technology, Dongguan 523106, China
2Financial Department, City College of Dongguan University of Technology, Dongguan 523106, China

Received 13 April 2012; Accepted 7 July 2012

Academic Editor: Hai L. Liu

Copyright © 2012 Quan-Ju Zhang and Xiao Qing Lu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.