Fuzzy data analysis — Methods and industrial applications

Many industrial problems require adequate interpretation of data which are present in the respective situations. For example process monitoring, diagnosis, quality control, and prediction are some of these tasks. All the related problems have in common that a large amount of data describing the resp...

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Bibliographic Details
Published in:Fuzzy sets and systems 1994-01, Vol.61 (1), p.19-28
Main Authors: Meier, Willi, Weber, Richard, Zimmermann, Hans-Jurgen
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Cluster analysis
Computer science
control theory
systems
Connectionism. Neural networks
data analysis
Exact sciences and technology
neural networks
pattern recognition
Pattern recognition. Digital image processing. Computational geometry
production and process control
quality control
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Summary:Many industrial problems require adequate interpretation of data which are present in the respective situations. For example process monitoring, diagnosis, quality control, and prediction are some of these tasks. All the related problems have in common that a large amount of data describing the respective area exists. But in most cases the information contained in the data is not used sufficiently. Since the above described problems have different characteristics a multitude of methods for analysing the existing data is needed to solve the related problems. In this article we give an overview over advanced methods for data analysis, present a software tool which supports the application of these methods, and show some industrial realizations to emphasize the benefits of advanced data analysis.
ISSN:0165-0114
1872-6801
DOI:10.1016/0165-0114(94)90280-1