摘要
对每次权值和阈值的调整均采用固定不变的学习率，是导致传统ＢＰ算法收敛速度慢的一个主要原因．本文从提高收敛速度及精度出发，对改进ＢＰ算法进行了深入研究：在ＢＰ算法中引入统计思想，给出相关系数的定义；基于相关系数，采用变学习率策略，提出两种学习率自适应调整算法，并将其具体应用于滚动轴承的故障诊断中．试验证明。
The constant learning rate is adopted for weights and thresholds adjustment each time is the main reason that results in low learning convergence speed of traditional BP algorithm. In this paper in order to improve convergence speed and accuracy of BP algorithm, the idea of statistics is introduced into BP algorithm and the definition of relevancy coefficient is given out. Based on relevancy coefficient and changeable learning strategy, two kinds of adaptive learning rate algorithms are put forward and applied to fault diagnosis of rollingcomponent bearings. The experiments show that this improved method improves more greatly than traditional BP algorithm in convergence speed.
出处
《河北工业大学学报》
CAS
1998年第4期23-27,共5页
Journal of Hebei University of Technology
基金
河北省自然科学基金
关键词
人工智能
神经网络
BP算法
滚动轴承
故障诊断
Artificial Intelligent, Neural Network, BP Algorithm, Rollingcomponent bearing,Fault Diagnosis
