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Least Squares One-Class Support Tensor Machine
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作者 Kaiwen Zhao Yali Fan 《Journal of Computer and Communications》 2024年第4期186-200,共15页
One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification ... One-class classification problem has become a popular problem in many fields, with a wide range of applications in anomaly detection, fault diagnosis, and face recognition. We investigate the one-class classification problem for second-order tensor data. Traditional vector-based one-class classification methods such as one-class support vector machine (OCSVM) and least squares one-class support vector machine (LSOCSVM) have limitations when tensor is used as input data, so we propose a new tensor one-class classification method, LSOCSTM, which directly uses tensor as input data. On one hand, using tensor as input data not only enables to classify tensor data, but also for vector data, classifying it after high dimensionalizing it into tensor still improves the classification accuracy and overcomes the over-fitting problem. On the other hand, different from one-class support tensor machine (OCSTM), we use squared loss instead of the original loss function so that we solve a series of linear equations instead of quadratic programming problems. Therefore, we use the distance to the hyperplane as a metric for classification, and the proposed method is more accurate and faster compared to existing methods. The experimental results show the high efficiency of the proposed method compared with several state-of-the-art methods. 展开更多
关键词 least Square One-Class support Tensor machine One-Class Classification Upscale least Square One-Class support vector machine One-Class support Tensor machine
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Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir 被引量:1
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作者 罗伟平 李洪奇 石宁 《Applied Geophysics》 SCIE CSCD 2016年第2期406-415,421,共11页
At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict th... At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area. 展开更多
关键词 Semi-supervised learning least squares support vector machine seismic attributes reservoir prediction
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Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature 被引量:6
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作者 Mohammad Mesbah Ebrahim Soroush Mashallah Rezakazemi 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第9期1238-1248,共11页
Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.... Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model. 展开更多
关键词 Hydrate formation temperature(HFT) Natural gas Sour gases least squares support vector machine Outlier diagnostics Leverage approach
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Modeling of Isomerization of C_8 Aromatics by Online Least Squares Support Vector Machine 被引量:7
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作者 李丽娟 苏宏业 褚建 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2009年第3期437-444,共8页
The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling... The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable. 展开更多
关键词 least squares support vector machine multi-variable ONLINE SPARSENESS ISOMERIZATION
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Temperature prediction control based on least squares support vector machines 被引量:5
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作者 BinLIU HongyeSU +1 位作者 WeihuaHUANG JianCHU 《控制理论与应用(英文版)》 EI 2004年第4期365-370,共6页
A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant i... A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm. 展开更多
关键词 Predictive control least squares support vector machines RBF kernel function Generalized prediction control
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Generalized Predictive Control with Online Least Squares Support Vector Machines 被引量:41
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作者 LI Li-Juan SU Hong-Ye CHU Jian 《自动化学报》 EI CSCD 北大核心 2007年第11期1182-1188,共7页
这份报纸基于能有效地处理非线性的系统的联机最少的广场支持向量机器(LS-SVM ) 建议一个实际概括预兆的控制(GPC ) 算法。在每个采样时期,算法递归地由增加新数据对并且在实时性质上从考虑删除最不重要的修改模型。删除的数据对被 lag... 这份报纸基于能有效地处理非线性的系统的联机最少的广场支持向量机器(LS-SVM ) 建议一个实际概括预兆的控制(GPC ) 算法。在每个采样时期,算法递归地由增加新数据对并且在实时性质上从考虑删除最不重要的修改模型。删除的数据对被 lagrange 的绝对值从最后一个采样时期更多样地决定。当增加新数据对并且删除存在的时,纸给模型参数的递归的算法分别地,一个大矩阵的倒置被避免,存储器能被算法完全控制。非线性的 LS-SVM 模型在每个采样时期在 GPC 算法被使用。抵销过程的 pH 上的概括预兆的控制的实验显示出建议算法的有效性和实物。 展开更多
关键词 普遍预测控制 支持向量机 联机模型 pH补偿过程 模糊控制
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Prediction of chaotic systems with multidimensional recurrent least squares support vector machines 被引量:2
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作者 孙建成 周亚同 罗建国 《Chinese Physics B》 SCIE EI CAS CSCD 2006年第6期1208-1215,共8页
In this paper, we propose a multidimensional version of recurrent least squares support vector machines (MDRLS- SVM) to solve the problem about the prediction of chaotic system. To acquire better prediction performa... In this paper, we propose a multidimensional version of recurrent least squares support vector machines (MDRLS- SVM) to solve the problem about the prediction of chaotic system. To acquire better prediction performance, the high-dimensional space, which provides more information on the system than the scalar time series, is first reconstructed utilizing Takens's embedding theorem. Then the MDRLS-SVM instead of traditional RLS-SVM is used in the high- dimensional space, and the prediction performance can be improved from the point of view of reconstructed embedding phase space. In addition, the MDRLS-SVM algorithm is analysed in the context of noise, and we also find that the MDRLS-SVM has lower sensitivity to noise than the RLS-SVM. 展开更多
关键词 chaotic systems support vector machines least squares noise
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Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine 被引量:3
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作者 XURui-Rui BIANGuo-Xin GAOChen-Feng CHENTian-Lun 《Communications in Theoretical Physics》 SCIE CAS CSCD 2005年第6期1056-1060,共5页
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we e... The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved. 展开更多
关键词 least squares support vector machine nonlinear time series PREDICTION CLUSTERING
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Sparse representation based on projection method in online least squares support vector machines 被引量:2
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作者 Lijuan LI Hongye SU Jian CHU 《控制理论与应用(英文版)》 EI 2009年第2期163-168,共6页
A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines (LS-SVM). The new inputs are projected... A sparse approximation algorithm based on projection is presented in this paper in order to overcome the limitation of the non-sparsity of least squares support vector machines (LS-SVM). The new inputs are projected into the subspace spanned by previous basis vectors (BV) and those inputs whose squared distance from the subspace is higher than a threshold are added in the BV set, while others are rejected. This consequently results in the sparse approximation. In addition, a recursive approach to deleting an exiting vector in the BV set is proposed. Then the online LS-SVM, sparse approximation and BV removal are combined to produce the sparse online LS-SVM algorithm that can control the size of memory irrespective of the processed data size. The suggested algorithm is applied in the online modeling of a pH neutralizing process and the isomerization plant of a refinery, respectively. The detailed comparison of computing time and precision is also given between the suggested algorithm and the nonsparse one. The results show that the proposed algorithm greatly improves the sparsity just with little cost of precision. 展开更多
关键词 least squares support vector machines PROJECTION SPARSITY pH neutralizing process ISOMERIZATION
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Fault diagnosis using a probability least squares support vector classification machine 被引量:4
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作者 GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China 《Mining Science and Technology》 EI CAS 2010年第6期917-921,共5页
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines ... Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM. 展开更多
关键词 fault diagnosis PROBABILITY least squares support vector classification machine roller bearing
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New predictive control algorithms based on Least Squares Support Vector Machines 被引量:3
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作者 刘斌 苏宏业 褚健 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第5期440-446,共7页
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlin... Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms. 展开更多
关键词 least squares support vector machines Linear kernel function RBF kernel function Generalized predictive control
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Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine 被引量:2
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作者 Aiqiang Pan Jian Zhou +2 位作者 Peng Zhang Shunfu Lin Jikai Tang 《Energy and Power Engineering》 2017年第4期713-724,共12页
An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady sta... An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability. 展开更多
关键词 CHAOTIC THEORY least squares support vector machine (LSSVM) Power Quality STEADY State Index Phase Space Reconstruction Particle SWARM Optimization
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Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines 被引量:1
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作者 王旭辉 黄圣国 +2 位作者 王烨 刘永建 舒平 《Journal of Southwest Jiaotong University(English Edition)》 2009年第1期22-26,共5页
Least squares support vector machine (LS-SVM) is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern sear... Least squares support vector machine (LS-SVM) is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern search method. Finally, by decoding aircraft communication addressing and reporting system (ACARS) report, a real-time cruise data set is acquired, and the diagnosis model is adopted to process data. In contrast to the radial basis function (RBF) neutral network, LS-SVM is more suitable for real-time diagnosis of gas turbine engine. 展开更多
关键词 Engine diagnosis Gas path least squares support vector machine Pattern search
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Least Squares-support Vector Machine Load Forecasting Approach Optimized by Bacterial Colony Chemotaxis Method 被引量:2
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作者 ZENG Ming LU Chunquan +1 位作者 TIAN Kuo XUE Song 《中国电机工程学报》 EI CSCD 北大核心 2011年第34期I0009-I0009,共1页
关键词 英文摘要 内容介绍 编辑工作 期刊
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Classification using least squares support vector machine for reliability analysis
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作者 郭秩维 白广忱 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2009年第7期853-864,共12页
In order to improve the efficiency of the support vector machine (SVM) for classification to deal with a large amount of samples, the least squares support vector machine (LSSVM) for classification methods is intr... In order to improve the efficiency of the support vector machine (SVM) for classification to deal with a large amount of samples, the least squares support vector machine (LSSVM) for classification methods is introduced into the reliability analysis. To reduce the coraputational cost, the solution of the SVM is transformed from a quadratic programming to a group of linear equations. The numerical results indicate that the reliability method based on the LSSVM for classification has higher accuracy and requires less computational cost than the SVM method. 展开更多
关键词 least squares support vector machine CLASSIFICATION RELIABILITY performancefunction
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Design of Ballistic Consistency Based on Least Squares Support Vector Machine and Particle Swarm Optimization
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作者 张宇宸 杜忠华 戴炜 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2015年第5期549-554,共6页
In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal f... In order to improve the firing efficiency of projectiles,it is required to use the universal firing table for gun weapon system equipped with a variety of projectiles.Moreover,the foundation of sharing the universal firing table is the ballistic matching for two types of projectiles.Therefore,a method is proposed in the process of designing new type of projectile.The least squares support vector machine is utilized to build the ballistic trajectory model of the original projectile,thus it is viable to compare the two trajectories.Then the particle swarm optimization is applied to find the combination of trajectory parameters which meet the criterion of ballistic matching best.Finally,examples show the proposed method is valid and feasible. 展开更多
关键词 ballistic matching least squares support vector machine particle swarm optimization curve fitting
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Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting
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作者 WANG Zilong XIA Chenxia 《Journal of Donghua University(English Edition)》 EI CAS 2019年第1期67-76,共10页
Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid mo... Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting. 展开更多
关键词 forecasting FRUIT FLY optimization algorithm(FOA) least squares support vector machine(LSSVM) SEASONAL index
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Thrust estimator design based on least squares support vector regression machine
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作者 赵永平 孙健国 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第4期578-583,共6页
In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based ... In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator. 展开更多
关键词 least squares support vector machine direct thrust control wrapper criterion
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MULTI-RESOLUTION LEAST SQUARES SUPPORT VECTOR MACHINES
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作者 Wang Liejun Zhang Taiyi Zhou Yatong 《Journal of Electronics(China)》 2007年第5期701-704,共4页
The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed alg... The Least Squares Support Vector Machines (LS-SVM) is an improvement to the SVM. Combined the LS-SVM with the Multi-Resolution Analysis (MRA),this letter proposes the Multi-resolution LS-SVM (MLS-SVM).The proposed algorithm has the same theoretical framework as MRA but with better approximation ability.At a fixed scale MLS-SVM is a classical LS-SVM,but MLS-SVM can gradually approximate the target function at different scales.In experiments,the MLS-SVM is used for nonlinear system identification,and achieves better identification accuracy. 展开更多
关键词 support vector machines (SVM) least square method Multi-Resolution Analysis (MRA) Nonlinear system identification
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ONLINE PARSIMONIOUS LEAST SQUARES SUPPORT VECTOR REGRESSION AND ITS APPLICATION 被引量:2
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作者 赵永平 孙健国 王健康 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2009年第4期280-287,共8页
A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression (LS-SVR), and the approach is called the OPLS-SVR for short. Hence, the response tim... A simple and effective mechanism is proposed to realize the parsimoniousness of the online least squares support vector regression (LS-SVR), and the approach is called the OPLS-SVR for short. Hence, the response time is curtailed. Besides, an OPLS-SVR based analytical redundancy technique is presented to cope with the sensor failure and drift problems to guarantee that the provided signals for the aeroengine controller are correct and acceptable. Experiments on the sensor failure and drift show the effectiveness and the validity of the proposed analytical redundancy. 展开更多
关键词 support vector machines SENSORS least squares analytical redundancy aeroengines
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