This paper presents a hybrid approach based on soft computing techniques in order to estimate ambient temperature for those places where such datum is not available. Indeed, we combine the Back- Propagation (BP) algorithm and the Simple Genetic Algorithm (GA) in order to effectively train neural networks in such a way that the BP algorithm initialises a few individuals of the GA's population. Experiments have been performed over all the available Italian places and results have shown a remarkable improvement in accuracy compared to the single and traditional methods. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Ceravolo, F., Felice, M. D., & Pizzuti, S. (2009). Combining back-propagation and genetic algorithms to train neural networks for ambient temperature modeling in italy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5484 LNCS, pp. 123–131). https://doi.org/10.1007/978-3-642-01129-0_16
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