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Neural Computing and Applications

Erschienen in:

01.03.2011 | Original Article

An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

verfasst von: Prasun Das, Indranil Banerjee

Erschienen in: Neural Computing and Applications | Ausgabe 2/2011

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Abstract

Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.

Vorheriger Artikel Curve fitting space for classification

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Titel: An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector
verfasst von: Prasun Das
Indranil Banerjee
Publikationsdatum: 01.03.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0443-z

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