Abstract
Artificial Neural networks have been found to be outstanding tools able to generate generalizable models in many disciplines. In this technical note, we present the multi-layer perceptron (MLP) which is the most common neural network.
See Chap. 2 about calibration.
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References
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford 482
Du K-L, Swamy MNS (2014) Neuronal networks and statistical learning. Springer, Berlin
Mas JF, Puig H, Palacio JL, Sosa AA (2004) Modelling deforestation using GIS and artificial neural networks. Environ Model Softw 19(5):461–471
Mas JF, Kolb M, Paegelow M, Camacho Olmedo MT, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111
Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural nets and gis to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26(6):553–575
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Taud, H., Mas, J. (2018). Multilayer Perceptron (MLP). In: Camacho Olmedo, M., Paegelow, M., Mas, JF., Escobar, F. (eds) Geomatic Approaches for Modeling Land Change Scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-60801-3_27
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DOI: https://doi.org/10.1007/978-3-319-60801-3_27
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