No abstract

Absfracl-This paper presents a novel algorithm, based on Least Squares Support Vector Machines (LS-SVM), to predict gasoline Octane number with near-infrared (NIR) spectroscopy. This algorithm not only has the same high generalization performance and global optimal solution as standard SVM, but also needs less computing time, which i s necessary to on-line application. Experimental results show that the proposed algorithm can obtain better prediction performance than regular algorithms such as Multivariate Linear Regression and Partial Least Squares.