Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Cited by Google
- Similars in SciELO
- Similars in Google
Share
Revista Facultad de Ingeniería Universidad de Antioquia
Print version ISSN 0120-6230On-line version ISSN 2422-2844
Abstract
VILLA, Fernán; VELASQUEZ, Juan and JARAMILLO, Patricia. Conrprop: an algorithm for nonlinear optimization with constraint. Rev.fac.ing.univ. Antioquia [online]. 2009, n.50, pp.188-194. ISSN 0120-6230.
Resilent Backpropagation is a gradient-based powerful optimization technique commonly used for training artificial neural networks, which is based on the use of a velocity for each parameter in the model. However, although this technique is able to solve unrestricted multivariate nonlinear optimization problems there are not references in the operations research literature. In this paper, we propose a modification of Resilent Backpropagation that allows us to solve nonlinear optimization problems subject to general nonlinear restrictions. The proposed algorithm is tested using six common used benchmark problems; for all cases, the constrained resilent backpropagation algorithm found the optimal solution and for some cases it found a better optimal point that the reported in the literature.
Keywords : nonlinear optimization; restrictions; backpropagation; rprop.