【摘要】 特征和训练样本选择是影响激光传感器故障诊断结果的两个重要因素,为了提高激光传感器故障诊断正确率,针对当前激光传感器故障诊断正确率低、可靠性差等不足,提出一种基于粗糙集和神经网络的激光传感器故障诊断模型。首先采用粗糙集理论对激光传感器特征进行约简,并把约简后特征向量作为神经网络的输入,然后采用粗糙集理论对训练样本集进行约简,减少了网络的训练次数,最后在Matlab 2012平台进行仿真实验。结果表明,本文模型提高了激光传感器故障诊断的正确率,减少了网络的训练时间,提高了激光传感器故障诊断的效率。更多还原

【Abstract】 Feature and training sample selection are two factors which affect the results of laser sensor fault diagnosis,In order to improve the accuracy of laser sensor fault diagnosis,aim at current defects like low accuracy and low relialility,a laser sensor fault diagnosis model based on rough set and neural network is proposed. Firstly,the features of laser sensor are selected by rough set theory,and the selected features are taken as the input of neural network,and then the rough set theory is used to reduce the training sample set and the training numbers the network are reduced,finally,the simulation experiments are carried out.The results show that the proposed model can improve the accuracy of fault diagnosis,reduce the training time of network,and improve the efficiency of fault diagnosis.更多还原