摘要
对随机邻域嵌入算法(SNE)中的距离进行改进,提出一种基于Manhattan距离的加权t-SNE(Mwt-SNE)算法。使用受空间维数影响较小的Manhattan距离作为度量方式,使用K-均值聚类算法将高维空间数据样本点距离分为三类,基于表格法进行权重参数寻优与加权,以加权相对Manhattan距离代替欧氏绝对距离计算相似度条件概率,从而增大数据对象之间的区分度,提升降维效果,增强分类显著性。提出基于Mwt-SNE算法的在线故障诊断模型,使用核密度估计(KDE)确定控制限并进行在线监控。TE化工过程实验表明,Mwt-SNE算法能有效降低误报率和漏报率,从而提高故障诊断稳定性和准确性。
This paper proposed a novel Manhattan distance based weighted t-SNE(Mwt-SNE)algorithm on the basis of improved distance in stochastic neighbor embedding(SNE).Firstly,it calculated samples Manhattan distances rather than Euclidean distance from high dimensional space for their less affections of dimension.Next,it divided these Manhattan distances into three groups with K-means clustering algorithm and implemented weighting processing separately with tabular parameter optimization method.Then it calculated similarity conditional probabilities with weighted Manhattan distances according to values category distribution.The aim of weighted Manhattan distances was to enlarge the data distinction,promote dimension reduction and enhance classification significance.Finally,it established an Mwt-SNE algorithm based on-line fault diagnosis model and the corresponding control limit with KDE.The experimental results on TE chemical process show that the proposed Mwt-SNE algorithm reduces FAR(false alarm rate)and MAR(missing alarm rate)as well as improves stability and accuracy.
作者
夏丽莎
方华京
Xia Lisha;Fang Huajing(School of Business,University of Shanghai for Science&Technology,Shanghai 200093,China;School of Automation,Huazhong University of Science&Technology,Wuhan 430074,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第7期2078-2081,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61473127,71572113)。
