Abstract
Neural Networks (NN) are usually trained with gradient search algorithms. Alternative approaches like genetic algorithms (GA) have been proposed before with promising results. In this paper six different training algorithms for NN are compared - two of them based on GA. The algorithms were evaluated with data from practically relevant applications of the Siemens AG.
Cite
CITATION STYLE
Heistermann, J. (1994). Different learning algorithms for neural networks - A comparative study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 866 LNCS, pp. 386–396). Springer Verlag. https://doi.org/10.1007/3-540-58484-6_282
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