Title :
Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns
Author_Institution :
Dept. of Manuf. & Ind. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia
Abstract :
Many of the previous research on the control chart pattern recognition were related to fully developed patterns. However, in practice, the process data will appear as a continuous stream of partially developed patterns. Such developing patterns are difficult to recognize since their structure are normally vague and dynamic. This study investigated the merit of a generalized single recognizer (all-class-one-network, ACON), a committee of specialized recognizers (one-class-one network, OCON) and the ensemble of ACON and OCON recognizers. These recognizers were embedded into a monitoring framework to enable on-line recognition. The performance of the schemes was evaluated based on percentage correct classification. The findings suggest that the ensemble of ACON and OCON recognizers with simple summation could significantly improve its discrimination capability. It is concluded that the strategy to configure and consolidate multiple recognizers is very important to achieve good classification performance.
Keywords :
control charts; neural nets; pattern classification; ACON; OCON; X-bar control chart pattern recognition; all-class-one-network; ensemble ANN-based recognizers; Artificial neural networks; Character recognition; Computers; Control charts; Industrial engineering; Manufacturing; Mechanical engineering; Multi-layer neural network; Pattern recognition; Process control; control chart; ensemble recognizers; pattern recognition; statistical process control;
Conference_Titel :
Industrial Engineering and Engineering Management, 2008. IEEM 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2629-4
Electronic_ISBN :
978-1-4244-2630-0
DOI :
10.1109/IEEM.2008.4738221