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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4224 LNCS, Issue , 2006, Pages 224-231

  Niska, Harri ^a   Heikkinen, Mikko ^a   Kolehmainen, Mikko ^a  

^a UNIVERSITY OF EASTERN FINLAND   (Finland)

Author keywords

Genetic algorithms; Input selection; Multi layer perceptron; Sensitivity analysis; Time series forecasting

Indexed keywords

GENETIC ALGORITHMS; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS; TIME SERIES ANALYSIS;

INPUT SELECTION; MULTILAYER PERCEPTRON MODEL (MLP); TIME SERIES FORECASTING;

AIR POLLUTION;

EID: 33750562314     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11875581_27     Document Type: Conference Paper

References (14)

1. Haykin, S.: Neural Networks: A Comprehensive Foundation. 2nd edn. Prentice Hall, Upper Saddle River NJ (1999)
2. Gardner, M.W., Dorling, S.R.: Artificial Neural Networks (the Multi-Layer Perceptron) - a Review of Applications in the Atmospheric Sciences. Atmospheric Environment 32 (1999) 2627-2636
3. Kohavi, R., John, G.H.: Wrappers for Feature Subset Selection. Artificial Intelligence 97 (1997) 273-324
4. Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., Cawley, G.: Extensive Evaluation of Neural Networks of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmospheric Environment 37 (2003) 4539-4550
5. Looney, C.G.: Pattern Recognition Using Neural Networks - Theory and Algorithms for Engineers and Scientists. Oxford University Press, Oxford New York (1997)
6. Belue, L.M., Bauer, K.W.: Determining Input Features for Multilayer Perceptrons. Neurocomputing 7 (1995) 111-121
7. Moody, J., Utans, J.: Principled Architecture Selection for Neural Networks: Application to Corporate Bond Rating Predictions. In: Moody, J., Hanson, S.J., Lippmann, R.P. (eds.): Proceedings of Advances in Neural Information Processing Systems. Morgan Kaufmann, San Mateo CA (1991) 683-690
8. Wilmott, C.J.: On the Validation of Models. Physical Geography 2 (1981) 184-194
9. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading Massachusetts (1989).
10. Oliveira L.S., Sabourin R., Bortolozzi F., and Suen C.Y.: A Methodology for Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit String Recognition. International Journal of Pattern Recognition and Artificial Intelligence 17 (2003) 903-930
11. Emmanouilidis, C., Hunter, A., MacIntyre, J.: A Multiobjective Evolutionary Setting for Feature Selection and a Commonality-Based Crossover Operator. In: Congress on Evolutionary Computation. IEEE, New Jersey (2000) 309-316
12. Fonseca, C. M., Fleming, P. J.: Genetic Algorithms for Multi-Objective Optimization: Formulation, Discussion and Generalization. In: Forrest, S. (eds.): 5th International Conference on Genetic Algorithms. Morgan Kauffman, California (1993) 416-423
13. Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J., Kolehmainen, M.: Evaluation of the Integrated Modelling System Containing a Multi-Layer Perceptron Model and the Numerical Weahter Prediction Model HIRLAM for the Forecasting of Urban Airborne Pollutant Concentrations. Atmospheric Environment 39 (2005) 6524-6536
14. Eerola, K.: The Operational HIRLAM at the Finnish Meteorological Institute. HIRLAM Newsletter 41 (2002) 19-24