Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series

Authors:
Harri Niska;Mikko Heikkinen;Mikko Kolehmainen
Affiliations:
Department of Environmental Sciences, University of Kuopio, Kuopio, Finland;Department of Environmental Sciences, University of Kuopio, Kuopio, Finland;Department of Environmental Sciences, University of Kuopio, Kuopio, Finland
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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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.