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Neuronale Netzwerkmodelle zur Analyse hochdimensionaler, multisensorischer Datensätze prozessierter Si-Wafer

URN to cite this document:
urn:nbn:de:bvb:355-opus-548
Schels, Armin
Date of publication of this fulltext: 17 Jan 2002 13:39


Abstract (German)

In dieser Arbeit werden verschiedene neuronale Netzwerkmodelle zur Analyse hochdimensionaler multisensorischer Datensätze prozessierter Silizium-Wafer untersucht. Für die Dimensionsreduzierung und Kennzahlenextraktion werden Methoden der Principal Component Analysis (PCA), Independent Component Analysis (ICA) und Backpropagation-Netzwerke verwendet. Zur anschließenden Klassifikation der ...

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Translation of the abstract (English)

Different neural network architectures were used for the analysis of high-dimensional and multi-sensoric datasets of processed silicon wafers. For dimensionality reduction and feature extraction layered networks with learning rules implementing a Principal Component Analysis (PCA), an Independent Component Analysis (ICA) and an error-backpropagation method were used. For the classification ...

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