DocumentCode
330319
Title
Neural networks as an aid to iterative optimization methods
Author
Li, H.J. ; Sung, A.H. ; Weiss, W.W. ; Wo, S.C.
Author_Institution
Dept. of Comput. Sci., New Mexico Tech., Socorro, NM, USA
Volume
2
fYear
1998
fDate
11-14 Oct 1998
Firstpage
1812
Abstract
This paper presents an approach of using neural networks to select starting points for iterative methods for optimization problems. Since input/output training data are often available or easily obtained from the problem description, a neural network can be trained to provide a rough model of the optimization problem. After the neural network is trained, it is used to select starting points for the iterative algorithm. We illustrate the potential of this approach with examples
Keywords
iterative methods; neural nets; optimisation; I/O training data; input/output training data; iterative optimization methods; neural networks; starting point selection; Artificial neural networks; Computer science; Data engineering; Iterative algorithms; Iterative methods; Neural networks; Nonlinear systems; Optimization methods; Predictive models; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
Conference_Location
San Diego, CA
ISSN
1062-922X
Print_ISBN
0-7803-4778-1
Type
conf
DOI
10.1109/ICSMC.1998.728158
Filename
728158
Link To Document