Xionglin Luo

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Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2(More)
For the on-line optimization problem of constrained model predictive control, constraints are considered. However, these considered constraints may cause it become a nonlinear control problem even for the linear plant and model. Therefore, it is difficult to analyze the properties of constrained model pre-dictive control. Based on the Newton control(More)