BP神经网络在油液污染与磨损预测中的应用

Application of BP Neural Network in Oil Pollution and Wear Prediction

针对传统BP算法存在的不足进行改进,采用共轭梯度法与Levenberg-Marquardt法对BP神经网络进行优化;通过实际数据进行预处理、建模分析,对比传统BP神经网络和经过优化后BP神经网络,证明了优化后的神经网络在油品污染与磨损的预测方面具有更好的泛化能力。

Aimed for improvement of the shortcomings existed in the traditional BP algorithm, the BP neural network was opti- mized by using conjugate gradient method and Levenberg-Marquardt method. Through the actual data pre-processing, modeling and a- nalysis, the traditional BP neural network and optimized BP neural networks were compared. It is proved that the optimized neural net- work has better generalization ability in the aspect of oil pollution and prediction of the wear.