Abstract
Two set theory methods, Interval and Affine Arithmetic, are used together with feedforward neural networks (FNN) in order to study their ability to perform state prediction in non-linear systems. Some fundamental theory showing the basic interval and affine arithmetic operations necessary to forward propagate through a FNN is presented and an application to a generic biotechnological process is performed confirming that due to the way the perturbations of the input data are considered, affine FNN perform better than interval ones.
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© 2005 Springer-Verlag Berlin Heidelberg
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Jamett, M., Acuña, G. (2005). Comparative Assessment of Interval and Affine Arithmetic in Neural Network State Prediction. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_73
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DOI: https://doi.org/10.1007/11427445_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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