Abstract
The performance of a method for the reduction of the input space dimensionality of a physical or engineering problem is analyzed. The results of its application to several engineering problems are compared with those obtained by other well-known methods for the reduction of input space dimensionality, such as Principal Component Analysis and Independent Component Analysis. In order to carry out this study, the features extracted by the three methods were used as inputs to a feedforward neural network. The advantages of the proposed method are that it presents a computational complexity depending on the number of variables and guarantees dimensional homogeneity in the new space.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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© 2005 Springer-Verlag Berlin Heidelberg
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Sánchez-Maroño, N., Fontenla-Romero, O., Castillo, E., Alonso-Betanzos, A. (2005). Modelling Engineering Problems Using Dimensional Analysis for Feature Extraction. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_150
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DOI: https://doi.org/10.1007/11550907_150
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