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An Approximate Linear Solver in least square support vector machine Using Randomized Singular Value Decomposition

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Wuhan University Journal of Natural Sciences 2015年第4期20卷 283-290页

作者： LIU Bing XIANG Hua School of Mathematics and Statistics Wuhan University

In this paper, we investigate the linear solver in least square support vector machine（LSSVM） for large-scale data regression. The traditional methods using the direct solvers are costly. We know that the linear equations should be solved repeatedly for choosing appropriate parameters in LSSVM, so the key for speeding up LSSVM is to improve the method of solving the linear equations. We approximate large-scale kernel matrices and get the approximate solution of linear equations by using randomized singular value decomposition（randomized SVD）. Some data sets coming from University of California Irvine machine learning repository are used to perform the experiments. We find LSSVM based on randomized SVD is more accurate and less time-consuming in the case of large number of variables than the method based on Nystrom method or Lanczos process.

关键词： least square support vector machine Nystr?m method Lanczos process randomized singular value decomposition

A predictive model of chemical flooding for enhanced oil recovery purposes:Application of least square support vector machine

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Petroleum 2016年第2期2卷 177-182页

作者： Mohammad Ali Ahmadi Maysam Pournik Department of Petroleum Engineering Ahwaz Faculty of Petroleum EngineeringPetroleum University of TechnologyAhwazIran Department of Petroleum Engineering Mewbourne School of Petroleum and Geological EngineeringUniversity of OklahomaOklahomaUnited States

Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent *** strategies of the aforementioned method are more robust and precise when they consider both economical point of views(net present value(NPV))and technical point of views(recovery factor(RF)).In the present study huge attempts are made to propose predictive model for specifying efficiency of chemical flooding in oil *** gain this goal,the new type of support vector machine method which evolved by Suykens and Vandewalle was ***,high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine *** to the mean square error(MSE),correlation coefficient and average absolute relative deviation,the suggested LSSVM model has acceptable reliability;integrity and ***,the proposed intelligent based model can be considered as an alternative model to monitor the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

关键词： Chemical flooding Enhanced oil recovery(EOR) Polymer Surfactant least square support vector machine (LSSVM)

Modeling NOx Emission of Coal-fired Boiler with Differential Evolution Optimized least square support vector machine

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Modeling NOx Emission of Coal-fired Boiler with Differential...

第30届中国控制与决策会议

作者： Zhenhao Tang Haiyang Zhang College of Automation Engineering Northeast Electric Power University

The NO emission is one of the major air pollutants of thermal power plants. Due to the complexity of the boiler production process, the NO emission was related with many parameters(such as, primary air flow, fuel flow). Building a NO emission prediction is the basis of combustion optimization and emission reduction. A least square support vector machine(LSSVM) model was developed and verified. Good prediction performance was achieved with the proper learning parameters which were chosen by a differential evolution(DE) algorithm. To illustrate the effectiveness of the DELSSVM, BP neural networks, Multi-layer perceptron(MLP) and partial least squares(PLS) regression algorithm were employed to make a comparison. Experimental results show that the DE optimized LSSVM(DELSSVM) has a promising performance in the prediction of the NO emission.

关键词： Coal-fired Boiler NOx Emission least square support vector machine Differential Evolution

Transformer Dissolved Gas Analysis Using least square support vector machine and Bootstrap

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Transformer Dissolved Gas Analysis Using Least Square Suppor...

第二十六届中国控制会议

作者： Shintemirov Almas Department of Electrical Engineering and Electronics The University of Liverpool

This paper presents a least square support vector machine（LS-SVM）approach to dissolved gas analysis（DGA）problems for power *** methods are employed to improve the diagnosis accuracy for DGA ***,bootstrap preprocessing is utilised to equalise the sample numbers for different fault ***,the preprocessed samples are inputted to a classier for fault *** comparison purposes,four classifiers are utilised,*** neural network（ANN）,K-nearest neighbour（KNN）,simple SVM and *** classification accuracy of LS-SVM is then compared with the ones of ANN,KNN and a simple *** results indicate that the LS-SVM approach can significantly improve the diagnosis accuracies for transformer fault classification.

关键词： Transformer Dissolved Gas Analysis least square support vector machine Bootstrap

least-square support vector machine and Wavelet Selection for Hearing Loss Identification

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 299-313页

作者： Chaosheng Tang Deepak Ranjan Nayak Shuihua Wang School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China Department of Computer Science and Engineering Malaviya National Institute of TechnologyJaipur302017India School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Hearing loss(HL)is a kind of common illness,which can significantly reduce the quality of *** example,HL often results in mishearing,misunderstanding,and communication ***,it is necessary to provide early diagnosis and timely treatment for *** study investigated the advantages and disadvantages of three classical machine learning methods:multilayer perceptron(MLP),support vector machine(SVM),and least-square support vector machine(LS-SVM)approach andmade a further optimization of the LS-SVM model via wavelet *** investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function and least-square ***,a wavelet selection method was proposed,and we found db4 can achieve the best *** experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89±1.77,which is superior to SVM *** results show that the least-square support vector machine is effective in hearing loss identification.

关键词： Hearing loss wavelet entropy multilayer perceptron least square support vector machine

Dioxin Emission Concentration Soft Measuring Method Based on Selective Ensemble least square support vector machine Algorithm

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Dioxin Emission Concentration Soft Measuring Method Based on...

第37届中国控制会议

作者： Jian Tang Junfei Qiao Zihao Guo Aijun Yan Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin（DXN） is a highly toxic and persistent pollutant discharged from municipal solid waste incineration（MSWI）.The first principal model of DXN is difficult to establish due to the complex physical and chemical characteristics of the incineration process. In the practical process, DXN emission concentration is off-line detected with monthly or seasonal *** at such small sample modeling problem, a soft measuring method based on selective ensemble（SEN） least square support vector machine（LSSVM） for modeling DXN emission concentration is proposed. At first, candidate training sub-samples are produced from original training samples. Then, different candidate sub-sub-models based on the same kernel parameter and regularization parameter are constructed by using LSSVM. Thirdly, ensemble sub-models are selected by using the genetic algorithm optimization tool box and prior knowledge. Finally, these ensemble sub-models are combined by using partial least squares algorithm in terms of reduction con-linearity among different prediction outputs. Simulation results show effectiveness of the proposed approach by using dataset in reference [18].

关键词： Municipal solid waste incineration (MSWI) dioxin (DXN) selective ensemble least square support vector machine soft measuring model

Multi-scale least square Wavelet support vector machine

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Multi-scale Least Square Wavelet Support Vector Machine

第24届中国控制与决策会议

作者： Wang Xianfang, Wu Ruihong, Cui Jinling 1. School of Computer & Information Technology Henan Normal University Xinxiang 453007

Original least square Wavelet support vector machine （LSSVM） algorithm can not reach desired precision in multi-scale regression. To solve the problem, a multi-scale wavelet LSSVM algorithm is proposed in this paper by using a wavelet kernel. Mexican-hat wavelet function is used as the support vector kernel function, and further the least square Wavelet support vector machine LS-WSVM algorithm is presented. On this basis, the global optimum of the multi-scale regression modeling problem can be obtained by solving a quadratic programming problem. As a result, the regression model can effectively approximate multi-scale signals. Therefore, LS-WSVM is an efficient modeling method and worth popularization and application by computer simulation results.

关键词： Wavelet least square support vector machine Multi-scale regression

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

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Chinese Journal of Mechanical Engineering 2021年第1期34卷 168-180页

作者： Chao Chen Fei Shen Jiawen Xu Ruqiang Yan School of Instrument Science and Engineering Southeast UniversityNanjing 210096China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an 710049China

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering ***,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more *** this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working *** on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are *** NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD *** experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working *** proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.

关键词： Gear fault diagnosis Model parameter transfer Varying working conditions least square support vector machine

LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

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Frontiers of Structural and Civil Engineering 2022年第7期16卷 871-881页

作者： Shiguo XIAO Shaohong LI Key Laboratory of High-speed Railway Engineering Ministry of EducationSouthwest Jiaotong UniversityChengdu 610031China Department of Geological Engineering Southwest Jiaotong UniversityChengdu 610031China

The failure criteria of practical soil mass are very complex,and have significant influence on the safety factor of slope *** Coulomb strength criterion and the power-law failure criterion are classically *** one has limited applicability owing to the noticeable difference between calculated predictions and actual results in some *** the work reported here,an analysis method based on the least square support vector machine(LSSVM),a machine learning model,is purposefully provided to establish a complex nonlinear failure criterion via iteration computation based on strength test data of the soil,which is of more extensive applicability to many problems of slope *** particular,three evaluation indexes including coefficient of determination,mean absolute percentage error,and mean square error indicate that fitting precision of the machine learning-based failure criterion is better than those of the linear Coulomb criterion and nonlinear power-law *** on the proposed LSSVM approach to determine the failure criterion,the limit equilibrium method can be used to calculate the safety factor of three-dimensional slope *** of results of the safety factor of two three-dimensional homogeneous slopes shows that the maximum relative errors between the proposed approach and the linear failure criterion-based method and the power-law failure criterion-based method are about 12%and 7%,respectively.

关键词： slope stability safety factor failure criterion least square support vector machine