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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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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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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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Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation 被引量:7
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作者 Senlin CHEN Zhenghong GAO +2 位作者 Xinqi ZHU Yiming DU Chao PANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第10期2499-2509,共11页
Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hy... Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hysteresis loop that exists in the actual maneuvering process of the aircraft.Here,an excitation input suitable for nonlinear system identification is introduced to model unsteady aerodynamic forces with any motion in the amplitude and frequency ranges based on the Least Squares Support Vector Machines(LS-SVMs).In the selection of the input form,avoiding the use of reduced frequency as a parameter makes the model more universal.After model training is completed,the method is applied to predict the lift coefficient,drag coefficient and pitching moment coefficient of the RAE2822 airfoil,in sine and sweep motions under the conditions of plunging and pitching at Mach number 0.8.The predicted results of the initial unstable process and the final stable process are in close agreement with the Computational Fluid Dynamics(CFD)data,demonstrating the feasibility of the model for nonlinear unsteady aerodynamics modeling and the effectiveness of the input design approach. 展开更多
关键词 Aerodynamics models Forced vibration Input design least squares support vector machines Nonlinear system System identification Unsteady aerodynamics
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Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines 被引量:5
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作者 Qing-chao WANG Jian-zhong ZHANG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2011年第1期25-35,共11页
This paper deals with Wiener model based predictive control of a pH neutralization process.The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed ... This paper deals with Wiener model based predictive control of a pH neutralization process.The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM).Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification.Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models,Laguerre-polynomial Wiener models,and linear Laguerre models.The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process.The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC,Laguerre-polynomial Wiener model based NMPC,and linear model predictive control (LMPC).Validation results show that the proposed NMPC outperforms the other three controllers. 展开更多
关键词 Wiener model Nonlinear model predictive control (NMPC) pH neutralization process Laguerre filters least squares support vector machines (LSSVM)
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Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm 被引量:2
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作者 Xin LIU Guo WEI +1 位作者 Jin-wei SUN Dan LIU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第4期497-503,共7页
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I... Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method. 展开更多
关键词 least squares support vector machine Total least squares Multifunctional sensor Signal reconstruction
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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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Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland 被引量:1
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作者 秦钟 于强 +2 位作者 李俊 吴志毅 胡秉民 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE EI CAS CSCD 2005年第6期491-495,共5页
Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a s... Least squares support vector machines (LS-SVMs), a nonlinear kemel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem. 展开更多
关键词 least squares support vector machines (LS-SVMs) Water vapor and carbon dioxide fluxes exchange Radial basis function (RBF) neural networks
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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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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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Improved adaptive pruning algorithm for least squares support vector regression 被引量:4
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作者 Runpeng Gao Ye San 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期438-444,共7页
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit... As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance. 展开更多
关键词 least squares support vector regression machine (LS- SVRM) PRUNING leave-one-out (LOO) error incremental learning decremental learning.
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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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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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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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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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Improved scheme to accelerate sparse least squares support vector regression
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作者 Yongping Zhao Jianguo Sun 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期312-317,共6页
The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in p... The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem. 展开更多
关键词 least squares support vector regression machine pruning algorithm iterative methodology classification.
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Robust least squares projection twin SVM and its sparse solution 被引量:1
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作者 ZHOU Shuisheng ZHANG Wenmeng +1 位作者 CHEN Li XU Mingliang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第4期827-838,共12页
Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsi... Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly. 展开更多
关键词 OUTLIERS robust least squares projection twin support vector machine(R-LSPTSVM) low-rank approximation sparse solution
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Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions 被引量:11
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作者 高栗 李夕兵 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第1期290-295,共6页
Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accu... Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one. 展开更多
关键词 tunnel boring machine(TBM) performance prediction rate of penetration(ROP) support vector machine(SVM) partial least squares(PLS)
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Prediction method for surface finishing of spiral bevel gear tooth based on least square support vector machine
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作者 马宁 徐文骥 +2 位作者 王续跃 魏泽飞 庞桂兵 《Journal of Central South University》 SCIE EI CAS 2011年第3期685-689,共5页
The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was ... The predictive model of surface roughness of the spiral bevel gear (SBG) tooth based on the least square support vector machine (LSSVM) was proposed.A nonlinear LSSVM model with radial basis function (RBF) kernel was presented and then the experimental setup of PECF system was established.The Taguchi method was introduced to assess the effect of finishing parameters on the gear tooth surface roughness,and the training data was also obtained through experiments.The comparison between the predicted values and the experimental values under the same conditions was carried out.The results show that the predicted values are found to be approximately consistent with the experimental values.The mean absolute percent error (MAPE) is 2.43% for the surface roughness and 2.61% for the applied voltage. 展开更多
关键词 pulse electrochemical finishing (PECF) surface roughness least squares support vector machine (LSSVM) PREDICTION
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Application of Least Squares Support Vector Machine for Regression to Reliability Analysis 被引量:18
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作者 郭秩维 白广忱 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2009年第2期160-166,共7页
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona... In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for... 展开更多
关键词 mechanism design of spacecraft support vector machine for regression least squares support vector machine for regression Monte Carlo method RELIABILITY implicit performance function
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