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Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting

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Artificial Neural Nets and Genetic Algorithms

Abstract

In this paper we look at the role that vertical fields can play in enhancing the performance of a feedforward neural network. Vertical fields help us to determine zones in the input space that are mapped onto the same output, they act in a similar way to kernels of linear mappings but in a nonlinear setting. In the paper we illustrate our ideas using data from a real application, namely forecasting atmospheric pollution for the town of Saint-Etienne in France.

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References

  1. D.W. Pearson, G. Dray, M. Mesbah and B. Vuillot (2000), Ozone, Systmes Dynamiques et “Shadowing”, Journées thématiques “Automatique et Environnement”, Nancy, France.

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  2. D.W. Pearson, M. Batton-Hubert and G.H. Garcia (2002), Predicting Ozone Peaks: A combined CBR and cell mapping approach, IEMSS02, Lugano, Switzerland.

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© 2003 Springer-Verlag Wien

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Pearson, D.W., Batton-Hubert, M., Dray, G. (2003). Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_18

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  • DOI: https://doi.org/10.1007/978-3-7091-0646-4_18

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-00743-3

  • Online ISBN: 978-3-7091-0646-4

  • eBook Packages: Springer Book Archive

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