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 predictive control. Based on the Newton control(More)
The dynamic model of fluid catalytic cracking unit (FCCU) based on first principle analysis was discussed and built. It was simplified by spatial discretization to be applied in the simulation system to meet the real time requirement. Dynamic simulation is a key technique in safety science and safety engineering field which can be used for process hazard(More)
Soft sensor software based on ANN (artificial neural network) using BP or RBF was developed to estimate unmeasured variables such as product quality online. Some important topics including how to determine the delay time, how to simulate the dynamic system were discussed and solved. We applied a 3 layers BP network to identify the delay time of nonlinear(More)
In chemical processes there commonly exist highly nonlinear processes such as polymerization, PH process. Traditional multiple model control only establishes linearized submodels on finite steady states, and the loss of linearized models on nonsteady states can't meet the requirement for rigorous models during transition. So this paper proposes a varying(More)
The constraints of output variables, input variables and intermediate variables exist widely in chemical process control. The inconsistency in different constraints may make constrained model predictive controller have no feasible solutions, which will bring harmful effect to practical production. To ensure the implementation of model predictive control,(More)
This paper investigates the problems of robust Hankel norm model reduction for uncertain neutral stochastic time-delay systems with time-varying norm-bounded parameter uncertainties appearing in the state matrices. For a given mean square asymptotically stable system, our purpose is to construct reduced-order systems, which approximate the original system(More)
Traditional supervised learning algorithms assume that the training data and the test data are drawn from the same probability distribution. Considering the assumption is not hold in modern applications of machine learning, domain adaptation problems are proposed when the data distribution in test domain is different from that in training domain. This paper(More)