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BACKPROPAGATION ALGORITHM ADAPTATION PARAMETERS USING LEARNING AUTOMATA
2001
International Journal of Neural Systems
Despite of the many successful applications of backpropagation for training multi-layer neural networks, it has many drawbacks. For complex problems it may require a long time to train the networks, and it may not train at all. Long training time can be the result of the non-optimal parameters. It is not easy to choose appropriate value of the parameters for a particular problem. In this paper, by interconnection of fixed structure learning automata (FSLA) to the feedforward neural networks, we
doi:10.1142/s0129065701000655
pmid:11574959
fatcat:zjytkl2w5rajvkogcw274dkmy4