Cited By
View all- Yang TLiao ZLu XWang H(2009)Quality Skyline in Sensor DatabaseProceedings of the 2009 First International Workshop on Database Technology and Applications10.1109/DBTA.2009.10(578-581)Online publication date: 25-Apr-2009
An approximate approach to constraint solving in soft sensing
Pages 1415 - 1416
Abstract
Soft sensing is usually presented as a constraint solving problem. In a manufacturing context, traditional methods of soft sensing have to face challenges in robustness and efficiency. In this paper, we proposed a granular-based approach to constraint solving for soft sensing. In our method, we first construct a granular-based soft sensing model, then estimate bounds of each granule, and finally solve this granulated problem with a smaller size. According to our analysis, this method is robust and efficient.
References
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Luigi Fortuna, Salvatore Graziani, Alessandro Rizzo.: Soft Sensors for Monitoring and Control of Industrial Processes. p1--13, Springer London (2007).
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Y. Li; Q. Li; H. Wang; N. Ma: Soft Sensing Based on LS-SVM and Its Application to a Distillation Column, Intelligent Systems Design and Applications, Oct. 2006. Vol. 1, P. 177--182
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Liu Z.; Lian X.; Wang Z.; Wang X.; Hou C. A Soft Sensing Method Based on Process Neural Network, 2006 IEEE International Conference on Information Acquisition, 20--23 Aug. 2006 Page(s): 611--616
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Yao Y Y. Granular computing: Basic issues and possible solutions. Proceedings of the 5th Joint Conference on Information Sciences. USA, 2000. 186--189
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Skowron A.,: Information Granules: Towards Foundations of Granular Computing, International Journal of Intelligent Systems, Vol. 16 (2001) 57--85
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Index Terms
- An approximate approach to constraint solving in soft sensing
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March 2009
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Published: 08 March 2009
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View all- Yang TLiao ZLu XWang H(2009)Quality Skyline in Sensor DatabaseProceedings of the 2009 First International Workshop on Database Technology and Applications10.1109/DBTA.2009.10(578-581)Online publication date: 25-Apr-2009
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