摘要
针对神经网络在故障诊断中存在着输入属性维数多和数据量庞大的缺点,利用粗糙集理论对原始数据进行约简,并剔除其中不必要的属性,构建了优化的粗糙集-神经网络智能系统。通过对实例分析,使用该系统能够提高采煤机故障诊断的准确性和效率,在故障诊断中有良好的应用前景。
To the condition of many input dimensions and lots of data in neural network fault diagnosis, some reductions from data based on rough sets theory are derived and unessential attributes are eliminated, an optimized rough set - neural network intelligent system is established. Through analyzing for instance, the accuracy and efficiency of shearer fault diagnosis can be enhanced by using the system, it is estimated that the optimized strategy may be further applied in fault diagnosis.
出处
《煤矿机械》
北大核心
2007年第9期188-190,共3页
Coal Mine Machinery
