Implementation of two-stage Hopfield model and its application in nonlinear systems
Loading...
Files
External sources
External sources
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Type
Article
Access right
Acesso restrito
Files
External sources
External sources
Abstract
This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.
Description
Keywords
Language
English
Citation
Artificial Intelligence and Soft Computing - Icaisc 2004. Berlin: Springer-verlag Berlin, v. 3070, p. 954-959, 2004.
