Abstract
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.
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Bartecki, K. (2012). Neural Network-Based PCA: An Application to Approximation of a Distributed Parameter System. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_1
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DOI: https://doi.org/10.1007/978-3-642-29347-4_1
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