Stability and convergence analysis of a neural model applied in nonlinear systems optimization
Loading...
External sources
External sources
Date
Authors
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Springer Nature
Springer Nature
Type
Article
Access right
Acesso restrito
External sources
External sources
Abstract
A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.
Description
Keywords
Language
English
Citation
Artificail Neural Networks and Neural Information Processing - Ican/iconip 2003. Berlin: Springer-verlag Berlin, v. 2714, p. 189-197, 2003.
