Abstract
The aim of this paper was to evaluate genetic algorithms (GA) and sensitivity analysis (SA) for selecting inputs of a multi-layer perceptron model (MLP) applied to forecast time-series of urban air pollutant. The main objective was to compare usability and efficiency of the methods. The results in general showed that the methods based on the SA and GA can be used efficiently to select relevant variables and thus, to enhance the performance of MLP.
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© 2006 Springer-Verlag Berlin Heidelberg
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Niska, H., Heikkinen, M., Kolehmainen, M. (2006). Genetic Algorithms and Sensitivity Analysis Applied to Select Inputs of a Multi-Layer Perceptron for the Prediction of Air Pollutant Time-Series. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_27
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DOI: https://doi.org/10.1007/11875581_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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