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| Soft Measurement Modeling and Chemical Application Based on ISOMAP-ELM Neural Network |
| LI Rong-yu,WANG Li-ming |
| College of Computer Science and Technology, Nanjing Tech University, Nanjing, Jiangsu 211816, China |
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Abstract In view of the chemical process, it is difficult to build a corresponding mechanism model, a soft sensor model based on ISOMAP-ELM is proposed. It combines ISOMAP with ELM, through the ISOMAP, the input data can reduce its dimensionality, eliminate the colinearity among each other, and extract more representative characteristics. Finally, the extracted feature components are sent to ELM to build a soft sensor model. Verification results show that the proposed algorithm has a high prediction precision,model squared error is only 0.28, and the hit rate of the soft sensor model is 94%, it can give guidance for chemical process.
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Received: 31 December 2014
Published: 29 July 2016
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| Fund:江苏省自然科学基金（12KJB510007） |
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［1］Zhang M, Ge Z, Song Z, et al. Global-local structure analysis model and its application for fault detection and identification［J］. Industrial & Engineering Chemistry Research, 2011, 50(11):6837-6848.
［2］Guo L, Hao J H, Liu M. An incremental extreme learning machine for online sequential learning problems［J］. Neurocomputing, 2014,128（5）:50-58.
［3］Xiao D, Wang J, Mao Z Z. The research on the modeling method of batch process based on OS-ELM-RMPLS［J］.Chemometrics and Intelligent Laboratory Systems, 2014,134（5）:118–122.
［4］Bin Shams M A, Budman H M, Duever T A. Fault detection, identification and diagnosis using CUSUM based PCA［J］. Chemical Engineering Science, 2011, 66（20）:4488–4498.
［5］Huang G B,Zhu Q Y,Siew C K. Extreme learning machine: Theory and applications［J］. Neurocomputing, 2006, 70（1-3）:489–501.
［6］Lan Y, Soh Y C, Huang G B. Ensemble of online sequential extreme learning machine［J］. Neurocomputing, 2009, 72（13-15）:3391–3395.
［7］Tenenbaum J B, Silva V D, Langford J C. A global geometric framework for nonlinear dimensionality reduction［J］. Science, 2000, 290: 2319-2323. |
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2016, Vol. 37 
