کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1181371 | 1491552 | 2013 | 10 صفحه PDF | دانلود رایگان |
• This paper proposes an ameliorated teaching–learning-based optimization — A-TLBO.
• The A-TLBO is applied to adjust least squares support vector machine (LS-SVM).
• A Noxemission model of a 330MW coal-fired boiler is built based on LS-SVM.
• The tuned LS-SVM model by A-TLBO shows very good generalization ability.
The teaching–learning-based optimization (TLBO) is a new efficient optimization algorithm. To improve the solution quality and to quicken the convergence speed and running time of TLBO, this paper proposes an ameliorated TLBO called A-TLBO and test it by classical numerical function optimizations. Compared with other several optimization methods, A-TLBO shows better search performance. In addition, the A-TLBO is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build NOx emissions model of a 330MW coal-fired boiler and obtain a well-generalized model. Experimental results show that the tuned LS-SVM model by A-TLBO has well regression precision and generalization ability.
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 126, 15 July 2013, Pages 11–20