Modeling of LS-SVM based on mixtures of kernels for MISO systems

Kongzhi yu Juece/Control and Decision

Volumn 20, Issue 4, 2005, Pages

  Zhu, Yan Fei ^a   Wu, Jian Ping ^a   Li, Qi ^a   Mao, Zong Yuan ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Kernels; Lithopone; LS SVM; Modeling

Indexed keywords

COMPUTER SIMULATION; LEAST SQUARES APPROXIMATIONS; MATRIX ALGEBRA; PREDICTIVE CONTROL SYSTEMS; ROBOT LEARNING;

KERNELS; LITHOPONE; LS-SVM; MISO SYSTEMS;

CALCINATION;

EID: 19044395306     PISSN: 10010920     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. Huang R T, Liu Y P, Mao Z Y, et al. Realization of process control system for lithopone kiln and calcinator[J]. J of South China University of Technology, 2003, 31(12): 42-45.
2. Vapnik V N. The nature of statistical learning theory[M]. New York; Springer-Verlag, 1995.
3. Bernhard S, Alexander J S. Learning with kernels-support vector machines, regularization, optimization and beyond[M]. Cambridge: The MIT Press, 2003.
4. Suykens J A, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.
5. Smits G F, Jordan E M. Improved SVM regression using mixtures of kernels[A]. IEEE Proc of the 2002 Int Joint Conf on Neural Networks[C]. Honolulu, 2002, 3: 2785-2790.
6. Smola A J, Schölkopf B. A tutorial on support vector regression[R]. London: University of London, 1998.
7. Smola A J. Learning with kernels[D]. Belin: Informatik der Technischen Universitat, 1998.
8. Schölkopf B, Mika S, Burges C J C, et al. Input space versus feature space in kernel-based methods[J]. IEEE Trans on Neural Networks, 1999, 10(5): 1000-1017.
9. Huang R T, Liu Y P, Di Z, et al. Development of process control system for lithopone and calcinator (II)-Preprocessing technology of sampling data[J]. J of South China University of Technology, 2002, 30(4): 52-55.