- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2014 (2014), Article ID 794368, 7 pages
Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
2School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Received 16 December 2013; Revised 14 January 2014; Accepted 14 January 2014; Published 27 February 2014
Academic Editor: Shen Yin
Copyright © 2014 Weili Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- S. Yin, H. Luo, and S. Ding, “. Real-time implementation of fault-tolerant control systems with performance optimization,” IEEE Transactions on Industrial Electronics, vol. 64, no. 5, pp. 2402–2411, 2014.
- Q.-D. Yang, F.-L. Wang, and Y.-Q. Chang, “Soft sensor of biomass based on improved BP neural network,” Control and Decision, vol. 23, no. 8, pp. 869–878, 2008.
- G. Liu, D. Zhou, H. Xu, and C. Mei, “Soft sensor modeling using SVM in fermentation process,” Chinese Journal of Scientific Instrument, vol. 30, no. 6, pp. 1228–1232, 2009.
- L. Huang, Y. Sun, X. Ji, Y. Huang, and B. Wang, “Soft sensor of lysine fermentation based on tPSO-BPNN,” Chinese Journal of Scientific Instrument, vol. 31, no. 10, pp. 2317–2321, 2010.
- S. J. Qin, “Recursive PLS algorithms for adaptive data modeling,” Computers and Chemical Engineering, vol. 22, no. 4-5, pp. 503–514, 1998.
- W. Li, H. H. Yue, S. Valle-Cervantes, and S. J. Qin, “Recursive PCA for adaptive process monitoring,” Journal of Process Control, vol. 10, no. 5, pp. 471–486, 2000.
- S. J. Qin and T. J. McAvoy, “Nonlinear PLS modeling using neural networks,” Computers and Chemical Engineering, vol. 16, no. 4, pp. 379–391, 1992.
- D. Dong and T. J. Mcavoy, “Nonlinear principal component analysis: based on principal curves and neural networks,” Computers and Chemical Engineering, vol. 20, no. 1, pp. 65–78, 1996.
- J. C. B. Gonzaga, L. A. C. Meleiro, C. Kiang, and R. Maciel Filho, “ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process,” Computers and Chemical Engineering, vol. 33, no. 1, pp. 43–49, 2009.
- S. Yin, G. Wang, and H. Karimi, “Data-driven design of robust fault detection system for wind turbines,” Mechatronics, 2013.
- V. N. Vapnik, The Nature Statistical Learning Theory, Springer, New York, NY, USA, 1999.
- V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999.
- G. Liu, D. Zhou, H. Xu, and C. Mei, “Microbial fermentation process soft sensors modeling research based on the SVM,” Chinese Journal of Scientific Instrument, vol. 30, no. 6, pp. 1228–1232, 2009.
- X.-J. Gao, P. Wang, C.-Z. Sun, J.-Q. Yi, Y.-T. Zhang, and H.-Q. Zhang, “Modeling for Penicillin fermentation process based on support vector machine,” Journal of System Simulation, vol. 18, no. 7, pp. 2052–2055, 2006.
- J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999.
- X. Wang, J. Chen, C. Liu, and F. Pan, “Hybrid modeling of penicillin fermentation process based on least square support vector machine,” Chemical Engineering Research and Design, vol. 88, no. 4, pp. 415–420, 2010.
- L. LI, H. SU, and J. CHU, “Modeling of isomerization of C8 aromatics by online least squares support vector machine,” Chinese Journal of Chemical Engineering, vol. 17, no. 3, pp. 437–444, 2009.
- L. Li, K. Song, and Y. Zhao, “Modeling of ARA fermentation based on affinity propagation clustering,” CIESC Journal, vol. 62, no. 8, pp. 2116–2121, 2011.
- J. W. Cao and H. W. Ma, Microbial Engineering, Science press, Beijing, China, 2002.
- G. Guo, S. Z. Li, and K. L. Chan, “Support vector machines for face recognition,” Image and Vision Computing, vol. 19, no. 9-10, pp. 631–638, 2001.
- R.-Q. Chen and J.-S. Yu, “Soft sensor modeling based on particle swarm optimization and least squares support vector machines,” Journal of System Simulation, vol. 19, no. 22, pp. 5307–5310, 2007.
- L. Huang, Y. Sun, X. Ji, Y. Huang, and T. Du, “Soft sensor modeling of fermentation process based on the combination of CPSO and LSSVM,” Chinese Journal of Scientific Instrument, vol. 32, no. 9, pp. 2066–2070, 2011.
- X. Zhang, “Using class-center vectors to build support vector machines,” in Proceedings of the 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP '99), pp. 3–11, August 1999.
- C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 464–471, 2002.
- S. Yin, S. Ding, A. Haghani, and H. Hao, “Data-driven monitoring for stochastic systems and its application on batch process,” International Journal of Systems Science, vol. 44, no. 7, pp. 1366–1376, 2013.
- Y. Liu and H.-Q. Wang, “Pensim simulator and its application in penicillin fermentation process,” Journal of System Simulation, vol. 18, no. 12, pp. 3524–3527, 2006.
- S. Yin, S. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process,” Journal of Process Control, vol. 22, no. 9, pp. 1567–1581, 2012.