Issues in development of artificial neural network-based control chart pattern recognition schemes
Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise in...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
EuroJournals Publishing, Inc. 2010
2010
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| Subjects: | |
| Online Access: | http://www.eurojournals.com/ejsr.htm http://www.eurojournals.com/ejsr.htm http://eprints.uthm.edu.my/8313/1/ejsr_39_3_04.pdf |
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| Summary: | Control chart pattern recognition has become an active area of research since late
1980s. Much progress has been made, in which there are trends to heighten the
performance of artificial neural network (ANN)-based control chart pattern recognition
schemes through feature-based and wavelet-denoise input representation techniques, and
through modular and integrated recognizer designs. There is also a trend to enhance it’s
capability for monitoring and diagnosing multivariate process shifts. However, there is a
lack of literature providing a critical review on the issues associated to such advances. The
purpose of this paper is to highlight research direction, as well as to present a summary of
some updated issues in the development of ANN-based control chart pattern recognition
schemes as being addressed by the frontiers in this area. The issues highlighted in this
paper are highly related to input data and process patterns, input representation, recognizer
design and training, and multivariate process monitoring and diagnosis. Such issues could
be useful for new researchers as a starting point to facilitate further improvement in this
area. |
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