摘要
针对BP神经网络算法的不足,利用径向基函数(RBF)神经网络建立设备的磨损预测模型,对光谱分析数据进行实例仿真,并与BP网络模型进行对比研究。仿真结果表明,该模型预测精度高,训练时间短,大大优于BP神经网络模型。
Due to the defect of BP algorithm, radial basis function （RBF） neural network was applied to establish the model of equipment wearing prediction. The model was used to predict oil spectral analysis data. Compared with BP neural network model,the results indicate that RBF neural network is superior to BP network in the aspects of accuracy and efficiency.
出处
《润滑与密封》
CAS
CSCD
北大核心
2009年第1期71-72,83,共3页
Lubrication Engineering
关键词
径向基函数
神经网络
磨损预测
光谱分析
radial basis function （RBF）
neural network
wearing prediction
spectral analysis
