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.
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© 1994 Springer-Verlag Berlin Heidelberg
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Heistermann, J. (1994). Different learning algorithms for Neural Networks — A comparative study. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_282
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DOI: https://doi.org/10.1007/3-540-58484-6_282
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