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Abstract
Dynamic soft-sensing model of diesel oil solidifying point (DOSP) in crude distillation unit (CDU) is proposed based on diagonal recurrent neural network (DRNN). Because of long time-delay of the DOSP measurements, multi-step-ahead predictions are obtained recursively by Levinson predictor and then used as input of DRNN. Simulation results on the actual industrial process data show that the proposed dynamic soft-sensing model took good effects practically and significantly diminished the time-delay of output value.
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Authors and Affiliations
Department of Automation, Tsinghua University, Beijng, 100084, P.R. China
Hui Geng, Zhihua Xiong, Shuai Mao & Yongmao Xu
- Hui Geng
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- Zhihua Xiong
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- Shuai Mao
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- Yongmao Xu
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Editor information
Editors and Affiliations
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China
Zhang Yi
Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A
Jacek M. Zurada
Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China
Bao-Liang Lu
School of Electrical and Electronic Engineering, University of Manchester, UK
Hujun Yin
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© 2006 Springer-Verlag Berlin Heidelberg
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Geng, H., Xiong, Z., Mao, S., Xu, Y. (2006). Dynamic Soft-Sensing Model by Combining Diagonal Recurrent Neural Network with Levinson Predictor. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_154
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