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

基于贝叶斯证据框架下支持向量机建模的迭代优化控制(英文)

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.

在纸，一个反复的方法被介绍给批进程的最佳的控制。通常为一个批过程获得一个精确机械学的模型是很困难的。因为支持向量机器为小样品，非线性，高尺寸和本地最小描绘的问题是强大的，支持向量回归模型为端点性质被要求的批过程的最佳的控制被开发。模型参数在贝叶斯的证据框架以内被选择。基于模型，一个反复的方法被用来利用批过程的重复性质决定最佳的操作政策。数字模拟证明反复的最佳的控制能通过重复改进进程性能。