Industrial Process Modeling Based on Online Learning Algorithm for Regression Least Squares Support Vector Machine

Abstract:

According to the necessity for researching the optimization of enterprise energy-consuming based on model, the identification method for energy-consuming link in enterprise production process was researched. In view of the existing problems of Least Squares Support Vector Machine (LS-SVM), a modeling method based on Liu-transformation and LS-SVM was proposed. Liu-transformation was applied to intelligent data analysis for extracting typical characteristics from the training samples, and then the data were trained to construct the energy-consuming model based on on-line LS-SVM algorithm. The simulation result showed that the presented modeling method has the advantages of shorter computing time, robust and better generalization ability.

• Periodical: Advanced Materials Research (Volumes 472-475)
• Pages: 505-509
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.472-475.505
• Online since: February 2012
• Authors: Yong Xu, Jian Wang
• Keywords: Energy-Consuming, Least Squares Support Vector Machines (Ls-SVM), Nonparametric Weighted Feature Extraction

References

[1] A method of modeling energy consumption processes for energy efficiency evaluation of enterprises[J]. Chinese High Technology Letters, 2008,18(1): pp.47-53.

[2] Chong-Wei CHEN. Prior-knowledge based neural network modeling and application [D]. Hangzhou: Zhejiang University, 2002.

[3] FENG SHU-HU，GUAN XIAO-JI．Energy output prediction model on time series analysis and neural network[C]. 2007 International Conference on Wireless Communications, Networking and Mobile Computing. [S.l.]: IEEE, 2007: 5021-5024.

DOI: 10.1109/wicom.2007.1230

[4] Ma Fumin, Wang Jian . Input-output model for enterprise energy-consuming unit based on improved resource allocating networks[J]. Computer Applications, 2008,28(10): 2499-04.

DOI: 10.3724/sp.j.1087.2008.02499

[5] J.A.K. Suykens, T.Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.

DOI: 10.1142/5089

[6] T. Van Gestel, J.A.K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor, J. Vandewalle, Benchmarking least squares support vector machine classifiers, Machine Learning 54(1) (2004) 5-32.

DOI: 10.1023/b:mach.0000008082.80494.e0

[7] Sohölkopf B, Smola A. Learning with Kernels. MIT Press, Cambridge, MA, 2002.

[8] CAMBELL C. Kernel methods: a survey of current techniques [J]. Neurocomputing, 2002, 48:63-84.

[9] Hsing-Chuan Liu, "A novel nonparametric weighted feature extraction transformation algorithm based on the outmost points", Journal of Taichung University(JNTCU), 2008, 22(1):1-7.

[10] TANG He-sheng, XUE Song-tao, CHEN Rong, el al. Sequential LS-SVM for structural systems identification[J]. Journal of Vibration Engineering, 2006,19(3): 382-387.

[11] SONG Xiaofeng, YU Huanjun, HU Shangxu. Modeling Delayed Coking Process by Adaptive Support Vector Machine[J]. Journal of Chemical Industry and Engineering, 2004, 55(1): 147-150.

[12] HUANG Haiyan, GU Xingsheng. Cultural Differential Evolution Algorithm and Its Application in Chemical Process Modeling[J]. CIESC Journal, 2009, 60(3): 668-674.