A Recurrent Neural Network for Nonlinear Fractional Programming
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-12
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Quan-Ju& Lu, Xiao Qing. 2012. A Recurrent Neural Network for Nonlinear Fractional Programming. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1001949
Modern Language Association (MLA)
Zhang, Quan-Ju& Lu, Xiao Qing. A Recurrent Neural Network for Nonlinear Fractional Programming. Mathematical Problems in Engineering No. 2012 (2012), pp.1-18.
https://search.emarefa.net/detail/BIM-1001949
American Medical Association (AMA)
Zhang, Quan-Ju& Lu, Xiao Qing. A Recurrent Neural Network for Nonlinear Fractional Programming. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1001949
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001949