基于特征加权的代理判别模型模式识别方法  

Pattern recognition method of agent discriminate model based feature weighted

作　　者：潘海洋[1,2] 郑近德[1] 郭雨怡 Pan Haiyang;Zheng Jinde;Guo Yuyi(School of Mechanical Engineering,Anhui University of Technology,Ma’anshan Anhui 243032,China;State Key Laboratory of Advanced Design & Manufacture for Vehicle Body,Hunan University,Changsha 410082,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243032 [2]湖南大学汽车车身先进设计制造国家重点实验室,长沙410082

出　　处：《计算机应用研究》2019年第6期1727-1730,共4页Application Research of Computers

基　　金：国家重点研发计划课题资助项目(2017YFC0805103);国家自然科学基金资助项目(51505002);安徽省自然科学基金资助项目(1708085QE107)

摘　　要：针对以往模式识别方法的不足及特征值贡献度的问题,提出了基于特征加权的代理判别模型(agent discriminate model based feature weighted,ADMFW)模式识别方法。该方法的核心在于利用加权因子获取加权特征,并采用代理模型建立加权特征之间的关系函数,即首先计算特征值的权值因子,评估特征值的显著度,进而对每个特征值予以权值;然后利用加权特征和代理模型建立预测模型;最后采用预测模型对未知样本进行识别诊断。对滚动轴承实测数据的分析结果表明,ADMFW可以有效地对滚动轴承的工作状态和故障类型进行识别。In view of the shortcomings of the previous pattern recognition methods and the saliency problem of features,this paper proposed a pattern recognition method-agent discriminate model based feature weighted(ADMFW).The core of this method is to use the weighting factor to obtain the weighted feature and utilize the agent discriminate model to establish the weighting function of weighted feature.Firstly,it calculated the weighting factors of the features to evaluate the saliency of the feature and assigning the weights to features.Secondly,it used the weighting features and the agent discriminate model to establish the prediction model.Finally,it applied the prediction model to identify the unknown samples.The analysis of the experimental data of rolling bearing shows that this method can identify the working states and fault types of rolling bearings effectively.

关 键 词：贡献度 ADMFW 权值因子 滚动轴承 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]