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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 researches.Developing 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 reservoirs.To gain this goal,the new type of support vector machine method which evolved by Suykens and Vandewalle was employed.Also,high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine model.According to the mean square error(MSE),correlation coefficient and average absolute relative deviation,the suggested LSSVM model has acceptable reliability;integrity and robustness.Thus,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 of Miniature Unmanned Helicopter for Hovering Status Based on least square support vector machine

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Modeling of Miniature Unmanned Helicopter for Hovering Statu...

2010 Third International Symposium on Knowledge Acquisition and Modeling (KAM 2010)

作者： Huang Degang 1 Wu Jiande 1 2 Fan Yugang 1 2 Wang Xiaodong 1 Feng Ting 1 (1) Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming (2) Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction Ministry of Education

Miniature unmanned helicopter （MUH） is controlled member which is very complicated. There are highly nonlinear, close coupled, time-variation, open-loop unstable etc. The paper introduces a method of model building for miniature unmanned helicopter （MUH）, based on least square support vector machine. Having been tested with practical flight data, the method of model building manifests well.

关键词： miniature unmanned helicopter （MUH） nonlinear close coupled least square support vector machine kernel function

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 transformers.Two methods are employed to improve the diagnosis accuracy for DGA analysis.Firstly,bootstrap preprocessing is utilised to equalise the sample numbers for different fault types.Then,the preprocessed samples are inputted to a classier for fault classification.For comparison purposes,four classifiers are utilised,i.***.artificial neural network（ANN）,K-nearest neighbour（KNN）,simple SVM and LS-SVM.The classification accuracy of LS-SVM is then compared with the ones of ANN,KNN and a simple SVM.The 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

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

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 periods.Aim 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

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 stability.The Coulomb strength criterion and the power-law failure criterion are classically simplified.Each one has limited applicability owing to the noticeable difference between calculated predictions and actual results in some cases.In 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 stability.In 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 criterion.Based 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 stability.Analysis 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

On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models

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Petroleum 2022年第3期8卷 424-435页

作者： Aleksander Lekomtsev Amin Keykhosravi Mehdi Bahari Moghaddam Reza Daneshfar Omid Rezvanjou Department of Oil and Gas Technologies Perm National Research Polytechnic UniversityPerm KraiRussia Faculty of Petroleum and Natural Gas Engineering Sahand University of TechnologyTabrizIran Department of Petroleum Engineering Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology(PUT)AhwazIran

There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs.The filtration volume can be diminished by adding different additives to the drilling fluids.Recently,nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids.However,there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids.Hence in this study,two powerful tools ELM(extreme learning machine)and PSO-LSSVM(particle swarm optimization-least square support vector machine)are applied to determine the effect of various nanoparticles on the filtration volume.The assessment of the models is carried out by computing the statistical parameters,and it is found that ELM has a greater ability to predict the filtration volumes,while PSO-LSSVM performs satisfactorily too.The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error(RMSE=0.2459),coefficient of determination(R^(2)=0.999),and mean relative error(MRE=2.028%)for the dataset.The statistical analysis shows that the suggested model can predict the filtration volume with great accuracy.Moreover,through sensitivity analysis of the input parameters,it is found that for a specified nanoparticle,the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process.

关键词： Nanoparticles Drilling mud Extreme learning machine Filtration volume least square support vector machine