信息与电子工程2012，Vol.10Issue(5)：574-578,5.
多样本状态加权合成的CGHMM训练算法
Training algorithm of HMM based on multi-sample state-weighted synthesis
摘要
Abstract
A training algorithm, state-weighted synthesis Continuous Gaussian mixture Hidden Markov Model(CGHMM) synthesized by several CGHMMs, is presented according to the states distribution of observations. It can solve the problem that it was difficult to provide enough training data due to too many parameters in HMM model. The proposed method is applied in bearing fault diagnosis, and the average training time of 12.86 s, diagnosis time of 0.189 s, and diagnosis rate of 90% are obtained. The method based on state-weighted synthesis CGHMM is effective and feasible in bearing fault diagnosis and has a great prospect for application.关键词
连续高斯混合密度隐马尔可夫模型/训练算法/状态加权合成Key words
Continuous Gaussian mixture Hidden Markov Model/ training algorithm/ state-weighted synthesis分类
信息技术与安全科学引用本文复制引用
陆汝华,李盛欣..多样本状态加权合成的CGHMM训练算法[J].信息与电子工程,2012,10(5):574-578,5.基金项目
国家自然科学基金资助项目(61103108) （61103108）
湖南省科技计划资助项目(2010FJ6028) （2010FJ6028）
湖南省教育科学"十二五"规划课题(XJK012CGD034) （XJK012CGD034）
