The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Support vector machines: theory and applications
Machine Learning and Its Applications
Credit scoring using support vector machines with direct search for parameters selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Uncertainty Analysis and Decision Making; Guest Editors: Yan-Kui Liu, Baoding Liu, Jinwu Gao
Fuzzy Least Square Support Vector Machine Applied to Detect Damage for Fiber Smart Structures
IITAW '08 Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Evolving least squares support vector machines for stock market trend mining
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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Accurate heat rate forecasting is very important in ensuring the economic, efficient, and safe operation of a steam turbine unit. The support vector machine (SVM) is a novel tool from the artificial intelligence field that has been successfully applied to heat rate forecasting. The least squares SVM (LS-SVM) is an improved algorithm based on the SVM. LS-SVM has minimal computational complexity and fast calculation. However, traditional LS-SVM, which was established by using offline data samples, can no longer accurately describe the actual system working condition, thereby resulting in problems when directly used in heat rate prediction. In this paper, a heat rate forecasting method based on online LS-SVM, which possesses dynamic prediction functions, is proposed. To avoid blindness and inaccuracy in parameter selection, the gravitational search algorithm (GSA) is used to optimize the regularization parameter @c and the kernel parameter @s^2 of the online LS-SVM modeling. The results confirm the efficiency of the proposed method.