摘要
目的 研究概率神经网络模型 ,并应用于故障诊断 .方法 对基于概率统计思想和 Bayes分类规则的概率神经网络模型、网络结构、算法及其特点进行分析 ,利用其进行故障诊断 ,并提出一种优化估计平滑因子的方法 .结果 概率神经网络可很好地诊断自行火炮发动机进行中油路和气路的故障 .
Aim\ To research Probabilistic Neural Network(PNN) model, and its application to fault diagnosis. Methods\ Based on probability statistics theory and Bayes Classification Rule, PNN model, network structure, algorithm, and their characteristic is analyzed, and applied to fault diagnosis. An optimization method to estimate smoothing parameters is established. Results\ It is very good to diagnose the oil and gas fault in Self propelled Gun Engine(SPGE) by using PNN. Conclusion\ Using PNN will get better effect in the field of pattern recognition and fault diagnosis.
出处
《华北工学院测试技术学报》
2000年第1期7-11,共5页
Journal of Test and Measurement Technology
关键词
概率神经网络
故障诊断
自行火炮发动机
probability neural networks
bayes classification rule
fault diagnosis
