Introduction to the theory of neural computation
Introduction to the theory of neural computation
Introduction to artificial neural systems
Introduction to artificial neural systems
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
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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.