The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will resu...The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.展开更多
Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is intr...Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.展开更多
针对投影孪生支持向量机(Projection Twin Support VectorMachine,PTSVM)在训练和求解过程中存在的问题,提出了一类改进的投影孪生支持向量机(Improved PTSVM),简称为IPTSVM.该文首先构造了改进的线性投影孪生支持向量机,然后利用核技...针对投影孪生支持向量机(Projection Twin Support VectorMachine,PTSVM)在训练和求解过程中存在的问题,提出了一类改进的投影孪生支持向量机(Improved PTSVM),简称为IPTSVM.该文首先构造了改进的线性投影孪生支持向量机,然后利用核技巧轻松将其推广到了非线性形式.本文的主要贡献有:(1)提出了投影孪生支持向量机的新模型,克服了原始PTSVM在训练之前需要求解两个逆矩阵的问题;(2)继承了传统SVM(Support VectorMachine)的精髓,利用核技巧直接将线性IPTSVM推广到非线性形式;(3)引入了一个新的参数,可以调节模型的性能,提高了IPTSVM的分类精度.实验结果表明,与PTSVM算法相比较,IPTSVM不仅提高了分类精度,而且克服了PTSVM的一些不足.展开更多
提出基于Laplacian双联最小二乘支持向量机(Laplacian Twin Least Squares Support Vector Machine,LapTLSSVM)半监督模式识别的新型早期故障诊断方法。用时、频域特征集广泛收集旋转机械不同早期故障的特征信息,再用提升半监督局部Fis...提出基于Laplacian双联最小二乘支持向量机(Laplacian Twin Least Squares Support Vector Machine,LapTLSSVM)半监督模式识别的新型早期故障诊断方法。用时、频域特征集广泛收集旋转机械不同早期故障的特征信息,再用提升半监督局部Fisher判别分析(Enhanced Semi-Supervised Local Fisher Discriminant Analysis,ESSLFDA)将高维时、频域特征集约简为具有更好类区分度的低维特征向量,并输入到Lap-TLSSVM中进行早期故障诊断。Lap-TLSSVM引入了包含大量无标签数据信息的流形规则实现半监督学习;其目标函数只含等式约束条件,且用共轭梯度法求解目标函数的线性方程组以加速训练过程。所提出的方法在训练样本非常稀少的情况下具有较高的诊断精度和计算效率。深沟球轴承早期故障诊断实验验证了该方法的有效性。展开更多
基金Hebei Province Key Research and Development Project(No.20313701D)Hebei Province Key Research and Development Project(No.19210404D)+13 种基金Mobile computing and universal equipment for the Beijing Key Laboratory Open Project,The National Social Science Fund of China(17AJL014)Beijing University of Posts and Telecommunications Construction of World-Class Disciplines and Characteristic Development Guidance Special Fund “Cultural Inheritance and Innovation”Project(No.505019221)National Natural Science Foundation of China(No.U1536112)National Natural Science Foundation of China(No.81673697)National Natural Science Foundation of China(61872046)The National Social Science Fund Key Project of China(No.17AJL014)“Blue Fire Project”(Huizhou)University of Technology Joint Innovation Project(CXZJHZ201729)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902218004)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201902024006)Industry-University Cooperation Cooperative Education Project of the Ministry of Education(No.201901197007)Industry-University Cooperation Collaborative Education Project of the Ministry of Education(No.201901199005)The Ministry of Education Industry-University Cooperation Collaborative Education Project(No.201901197001)Shijiazhuang science and technology plan project(236240267A)Hebei Province key research and development plan project(20312701D)。
文摘The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.
基金supported by the Fundamental Research Funds for the Central Universities(No.NS2016027)
文摘Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.
文摘提出基于Laplacian双联最小二乘支持向量机(Laplacian Twin Least Squares Support Vector Machine,LapTLSSVM)半监督模式识别的新型早期故障诊断方法。用时、频域特征集广泛收集旋转机械不同早期故障的特征信息,再用提升半监督局部Fisher判别分析(Enhanced Semi-Supervised Local Fisher Discriminant Analysis,ESSLFDA)将高维时、频域特征集约简为具有更好类区分度的低维特征向量,并输入到Lap-TLSSVM中进行早期故障诊断。Lap-TLSSVM引入了包含大量无标签数据信息的流形规则实现半监督学习;其目标函数只含等式约束条件,且用共轭梯度法求解目标函数的线性方程组以加速训练过程。所提出的方法在训练样本非常稀少的情况下具有较高的诊断精度和计算效率。深沟球轴承早期故障诊断实验验证了该方法的有效性。
