Principal Component Analysis (PCA) has been widely applied to identify the sources of dimensional variation in automotive body assembly; however, current PCA methods based on a covariance matrix are not appropriate for dealing with high-dimensional data, noisy data, and missing data. Due to its adaptive nature and fast computational capability, an artificial neural network has been considered as a new alternative to overcome the statistical analysis problems. In this paper, an artificial neural network using a nonlinear transfer function will be introduced to overcome current data analysis problems in an auto assembly process. A case study is used to demonstrate the application of the proposed approach.


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    Title :

    Improving principal component analysis (PCA) in automotive body assembly using artificial neural networks


    Contributors:
    Jang, K. (author) / Yang, K. (author)

    Published in:

    Publication date :

    2001


    Size :

    10 Seiten, 8 Bilder, 2 Tabellen, 11 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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