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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 (2002) Neuronale Netzwerkmodelle zur Analyse hochdimensionaler, multisensorischer Datensätze prozessierter Si-Wafer. PhD, Universität Regensburg

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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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Item Type:Thesis of the University of Regensburg (PhD)
Date:16 January 2002
Referee:Elmar W. Lang
Date of exam:6 December 2001
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Keywords:Neuronales Netz , Zeitreihenanalyse , Halbleiterindustrie , , neural networks , fault detection , time series analysis
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Owner: Universitätsbibliothek Regensburg
Deposited On:21 Oct 2009 13:39
Last Modified:13 Mar 2014 11:08
Item ID:9899
Owner Only: item control page

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