摘要
通过对目标数据修正的原理进行分析,提出了一种新颖的基于目标数据修正的差异性神经网络集成方法.该方法利用对个体网络期望输出的动态修正,将其作为新的训练集,引导个体网络实现差异性学习.将其应用于变压器故障诊断,实验结果表明,该方法的故障诊断准确率优于个体网络独立训练的神经网络集成方法;与ADL(active diverse learning)方法相比,大大减少了集成网络的通信成本,是一种更为高效的神经网络集成方法.
A novel object data correction based diversity neural network ensemble method is proposed by analyzing the object data correction principle. In this method, the individual networks are trained by correcting expectative output of individual network dynamically and set as new training sets, and all the individual networks in the ensemble are guided to realize diversity learning. The method is applied to the fault diagnosis of power transformer, the experiment results show that accuracy rate of the method is superior to the neural network ensemble method which is trained by individual networks, in comparison with ADL （active diverse learning）, the communication cost of ensemble network is greatly reduced, it is a extremely efficient neural network ensemble method.
出处
《信息与控制》
CSCD
北大核心
2013年第1期58-63,70,共7页
Information and Control
基金
国家自然科学基金资助项目(60835004)
湖南省教育厅资助项目(10B109
10C1266)
湖南省科技计划资助项目(2011FJ3183)
湘潭大学校级资助项目(10XZX20)
关键词
目标数据修正
差异性神经网络
神经网络集成方法
object data correction
diversity neural network
neural network ensemble method
