In this article, an approximation of the spatiotemporal response of a distributed parameter system (DPS) with the use of the neural network-based principal component analysis (PCA) is considered. The presented approach is carried out using two different neural structures: single-layer network with unsupervised, generalized Hebbian learning (GHA-PCA) and two-layer feedforward network with supervised learning (FF-PCA). In each case considered, the effect of the number of units in the network projection layer on the mean square approximation error (MSAE) and on the data compression ratio is analysed. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bartecki, K. (2012). Neural network-based PCA: An application to approximation of a distributed parameter system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7267 LNAI, pp. 3–11). https://doi.org/10.1007/978-3-642-29347-4_1
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