In this paper the evolutionary design of a neural network model for predicting nonlinear systems behavior is discussed. In particular, the Breeder Genetic Algorithms are considered to provide the optimal set of synaptic weights of the network. The feasibility of the neural model proposed is demonstrated by predicting the Mackey-Glass time series. A comparison with Genetic Algorithms and Back Propagation learning technique is performed.
CITATION STYLE
De Falco, I., Iazzetta, A., Natale, P., & Tarantino, E. (1998). Evolutionary neural networks for nonlinear dynamics modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1498 LNCS, pp. 593–602). Springer Verlag. https://doi.org/10.1007/bfb0056901
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