通过对模糊神经网络训练，建立干扰和半导体生产线状态等输入参数与优化的重调度策略输出之间的映射关系。

The relation between the input of FNN, such as disturbance, system state parameters, and output of FNN, optimal rescheduling strategy, is built by FNN.

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我们通过仿真实验来评价重调度策略，仿真结果证明在系统中存在多个故障的情况下，重调度策略可以得到很好的结果。

We evaluate the performance of rescheduling policy and the experiment results prove the rescheduling policy is able to achieve better performance when there are many faults in the system.

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将重调度策略划分为半导体生产线、设备组和设备重调度层次，利用仿真评价确定优化的重调度策略，并获得样本数据。

The rescheduling strategies are divided into line layer, machine group layer and machine layer. The samples for FNN are setup by simulation evaluation.

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