摘要
为了对半导体生产线进行预测控制,首先研究了利用RBF神经网络对半导体生产线建立预测模型的方法;然后利用马氏决策过程递阶方法的思路,将系统状态分成若干互不相交的区间,系统状态所属区间改变时,根据预测模型的输出,即预测的系统状态,利用所提出的智能控制算法确定将要采取的投料策略和调度策略组合对生产线进行控制.实验表明,该算法的总体性能优于若干其他常用算法.
A predictive control problem is discussed via radial basis functions neural network (RBFNN). First RBFNN is introduced to build a predictive model for semiconductor manufacturing system. And then the hierarchical solution of Markov decision processes is used to divide state space into several independent units. When the state unit transfers, the optimized combination of release control and scheduling policy is achieved by the proposed intelligent control algorithm. The emulations show that the predictive control algorithm outgoes some other heuristic algorithms.
出处
《控制与决策》
EI
CSCD
北大核心
2006年第3期336-338,351,共4页
Control and Decision
基金
973基金项目(20002CB312202-03)
国家自然科学基金项目(60374005
60343002)
关键词
半导体生产线
RBF神经网络
递阶方法
遗传算法
Semiconductor manufacturing system
RBF neural network
Hierarchical solution
Genetic algorithm