基于智能互补融合观点，针对往复式空压机故障信息系统的不准确性和不完整性，设计了用于空压机故障分类的粗网络专家系统，对空压机系统故障做出准确的诊断。
The rough neural expert system is discussed in detail in this paper, which is applied to such an inaccurate system as the fault information of reciprocating air compressors.
由于风速测量的不准确性以及很难获得风力发电系统的精确模型，故采用传统的PID控制器难以在风速快速变化的情况下实现良好的控制效果。
Because of the wind speed measurement and accurate system model are hard to obtain, so it's difficult to achieve good results by using the traditional pid controller in case of rapidly variable wind.
该系统能够对磁通的变化及转差频率指令的不准确性进行补偿。
It can also compensate the change in flux and the inaccuracy in slip frequency instruction.
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