摘要
针对概率神经网络(PNN)模型强大的非线性分类能力,PNN能够很好地对变压器故障进行分类;文章通过对PNN神经网络的结构和原理的分析,应用PNN概率神经网络方法对变压器故障进行诊断;通过实例仿真表明,PNN网络的训练时间比BP网络少,比之预测准确度也要高,而且还具有高度的泛化能力,这使得PNN网络可以有效地运用到变压器故障诊断中,具有一定的可操作性。
Abstract： For the probabilistic neural network （PNN） model of strong non--linear classification capability, PNN can be good for trans- former fault classification. Based on the PNN neural network structure and principles of analysts, application PNN probabilistie neural net- work approach to transformer fault diagnosis, simulation and by example, we can draw , PNN network compared with the traditional BP neural networks, not only better in training speed and accuracy advantages, but also has a high degree of generalization ability, which makes the PNN network can be effectively applied to the transformer fault diagnosis; has a certain operational and practical value.
出处
《计算机测量与控制》
CSCD
北大核心
2012年第7期1760-1762,共3页
Computer Measurement &Control
关键词
变压器
概率神经网络
故障诊断
transformer
probabilistic neural network
fault diagnosis
