基于贝叶斯证据框架下支持向量机建模的迭代优化控制

In the paper,an iterative method is presented to the optimal control of batch processes.Generally it is very difficult to acquire an accurate mechanistic model for a batch process.Because support vector machine is powerful for the problems characterized by small samples,nonlinearity,high dimension and local minima,support vector regression models are developed for the optimal control of batch processes where end-point properties are required.The model parameters are selected within the Bayesian evidence framework.Based on the model,an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy.Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.

Iterative optimal control based on support vector machine modeling within the Bayesian evidence framework

In the paper,an iterative method is presented to the optimal control of batch processes.Generally it is very difficult to acquire an accurate mechanistic model for a batch process.Because support vector machine is powerful for the problems characterized by small samples,nonlinearity,high dimension and local minima,support vector regression models are developed for the optimal control of batch processes where end-point properties are required.The model parameters are selected within the Bayesian evidence framework.Based on the model,an iterative method is used to exploit the repetitive nature of batch processes to determine the optimal operating policy.Numerical simulation shows that the iterative optimal control can improve the process performance through iterations.