DocumentCode
2611036
Title
Refinements in training schemes for the Coulomb Energy network
Author
Vassilopoulos, John F. ; Koutsougeras, Cris
Author_Institution
Center for Bioenviron. Res., Tulane Univ., New Orleans, LA, USA
fYear
1996
fDate
16-19 Nov. 1996
Firstpage
194
Lastpage
199
Abstract
We discuss the interesting perspective offered by the Coulomb Energy network and we identify certain disadvantages with the existing approach to training it. We address these problems by constraining its architecture (topology) and offer a derivation of the new associated training algorithm. We study further refinements of this algorithm. Most notably, existing genetic algorithms are employed as initial search techniques and simulation results are provided.
Keywords
feedforward neural nets; genetic algorithms; learning (artificial intelligence); multilayer perceptrons; neural net architecture; search problems; Coulomb Energy network; feedforward neural network; genetic algorithms; learning model; multilayer network; search techniques; simulation results; topology; training algorithm; training scheme refinement; Aggregates; Clustering algorithms; Curve fitting; Genetic algorithms; Intelligent networks; Network topology; Neural networks; Pattern recognition; Robustness; Space charge;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools with Artificial Intelligence, 1996., Proceedings Eighth IEEE International Conference on
ISSN
1082-3409
Print_ISBN
0-8186-7686-7
Type
conf
DOI
10.1109/TAI.1996.560451
Filename
560451
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