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
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
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
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
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
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
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
The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very ...The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.展开更多
In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support ve...In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.展开更多
Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop cra...Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts: crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi (KLT) detector. Matrix-pattern-oriented least squares support vector machine (MatLSSVM), as the matrixization version of least square support vector machine (SVM), inherits the advantages of least squares support vector machine (LSSVM), reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.展开更多
Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise....Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.展开更多
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
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
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
Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is ex...Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.展开更多
The uniaxial compressive strength(UCS)of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system.The most commonly used method for determin...The uniaxial compressive strength(UCS)of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system.The most commonly used method for determining the UCS in laboratory settings is expensive and time-consuming.For this reason,UCS can be estimated using an indirect determination method based on several simple laboratory tests,including point-load strength,rock density,longitudinal wave velocity,Brazilian tensile strength,Schmidt hardness,and shore hardness.In this study,six data sets of indices for different rock types were utilized to predict the UCS using three nonlinear combination models,namely back propagation(BP),particle swarm optimization(PSO),and least squares support vector machine(LSSVM).Moreover,the best prediction model was examined and selected based on four performance prediction indices.The results reveal that the PSO–LSSVM model was more successful than the other two models due to its higher performance capacity.The ratios of the predicted UCS to the measured UCS for the six data sets were 0.954,0.982,0.9911,0.9956,0.9995,and 0.993,respectively.The results were more reasonable when the predicted ratio was close to a value of approximately 1.展开更多
Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.La...Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.Laser-induced breakdown spectroscopy(LIBS)is an advanced analytical technique for GOs certification,due to the fast multi-elemental analysis requiring minimal sample pretreatment.In this study,LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyzes.The spectral data was analyzed by two chemometric algorithms,i.e.principal component analysis(PCA)and least squares support vector machines(LS-SVM).Specifically,the performance of LS-SVM with least kernel and radial basis function(RBF)kernel was explored in sensitivity and robustness tests.Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance(a little inferior)in robustness test.Generally,LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel.The result indicated the potential for future applications in herbs and food,especially for in situ GOs applications of TCM authenticity rapidly.展开更多
This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration...This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.展开更多
The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation meth...The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task.Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost.This work sorts out and collects 60 sets of measured data of highway engineering;establishes an expressway cost index system based on 10 factors,including main route mileage,roadbed width,roadbed earthwork,and number of bridges;and processes the data through principal component analysis(PCA)and hierarchical cluster analysis.Particle swarm optimization(PSO)is used to obtain the optimal parameter combination of the regularization parameter c and the kernel function width coefficientin least squares support vector machine(LSSVM).Results show that the average relative and mean square errors of the PCA-PSO-LSSVM model are 0.79%and 10.01%,respectively.Compared with BP neural networks and unoptimized LSSVM model,the PCA-PSO-LSSVM model has smaller relative errors,better generalization ability,and higher prediction accuracy,thereby providing a new method for highway cost prediction in complex environments.展开更多
A method which adopts the combination of least squares support vector machine(LS-SVM) and Monte Carlo(MC) simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the traini...A method which adopts the combination of least squares support vector machine(LS-SVM) and Monte Carlo(MC) simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua(FLAC).The decimal ant colony algorithm(DACA) is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.展开更多
文摘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.
基金This work has been supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,China.
文摘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.
基金supported by the National Creative Research Groups Science Foundation of China (NCRGSFC:60721062)National Basic Research Program of China (973 Program) (No.2007CB714000)
文摘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.
基金The National High Technology Research and Development Program of China (No.2006AA12A108)
文摘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.
基金supported by the National Natural Science Foundation of China(No.51006052)
文摘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.
文摘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.
基金Project (No.60574022) supported by the National Natural Science Foundation of China
文摘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.
基金Sponsored by Provincial Natural Science Foundation of Henan of China(200612001)
文摘The prediction of the alkalinity is difficult during the sintering process. Whether or not the level of the alkalinity of sintering process is successful is directly related to the quality of sinter. There is no very good method for predicting the alkalinity by now owing to the high complexity, high nonlinearity, strong coupling, high time delay, and etc. Therefore, a new technique, the grey squares support machine, was introduced. The grey support vector machine model of the alkalinity enabled the development of new equation and algorithm to predict the alkalinity. During modelling, the fluctuation of data sequence was weakened by the grey theory and the support vector machine was capable of processing nonlinear adaptable information, and the grey support vector machine has a combination of those advantages. The results revealed that the alkalinity of sinter could be accurately predicted using this model by reference to small sample and information. The experimental results showed that the grey support vector machine model was effective and practical owing to the advantages of high precision, less samples required, and simple calculation.
基金This work was financially supported by the project of National Science and Technology Supporting Plan(2015BAF01B02)the Open Foundation of Intelligent Robots and Systems at the University of Beijing Institute of Technology,High-tech Innovation Center(2016IRS03).
文摘In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment,a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine(LS-SVM)was proposed.Firstly,bilateral filter was used to filter the RGB channels image respectively to eliminate noise.Then the pixel-level color feature and texture feature of the image,which was used as input of LS-SVM model(classifier)and SVM model(classifier),were extracted via RGB value of image and gray level co-occurrence matrix.Finally,the color image was segmented with the trained LS-SVM model(classifier)and SVM model(classifier)separately.The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting,back-lighting and overshadow,with the accuracy of above 90%.When segmenting an image,the SVM algorithm required 1.3 s,while the LS-SVM algorithm proposed in this paper just needed 0.7 s,which was better than the SVM algorithm obviously.The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81%picking success rate.
基金co-supported by the National Natural Science Foundation of China (No. 61203170)the Fundamental Research Funds for the Central Universities (No. NS2012026)Startup Foundation for Introduced Talents of Nanjing University of Aeronautics and Astronautics (No. 1007-YAH10047)
文摘Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts: crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi (KLT) detector. Matrix-pattern-oriented least squares support vector machine (MatLSSVM), as the matrixization version of least square support vector machine (SVM), inherits the advantages of least squares support vector machine (LSSVM), reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.
基金the Natural Science Foundation of Liaoning Province,China(20180550067)Liaoning Province Ministry of Education Scientific Study Project(2020LNZD06 and 2017LNQN11)University of Science and Technology Liaoning Talent Project Grants(601011507-20 and 601013360-17).
文摘Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.
基金supported by the National Natural Science Foundation of China (61074127)
文摘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.
基金supported by the National Natural Science Foundation of China(50576033)
文摘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.
基金supported by the National Natural Science Foundation of China(6177202062202433+4 种基金621723716227242262036010)the Natural Science Foundation of Henan Province(22100002)the Postdoctoral Research Grant in Henan Province(202103111)。
文摘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.
基金supported by the National Natural Science Foundation of China(7190121061973310).
文摘Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.
基金funded by the Science and technology program of Xizang Autonomous Region(Nos.XZ202301YD0034C and XZ202202YD0007C)the National Natural Science Foundation of China(Grant No.42002268)Open Fund of Badong National Observation and Research Station of Geohazards(No.BNORSG-202204).
文摘The uniaxial compressive strength(UCS)of rocks is a critical index for evaluating the mechanical properties and construction of an engineering rock mass classification system.The most commonly used method for determining the UCS in laboratory settings is expensive and time-consuming.For this reason,UCS can be estimated using an indirect determination method based on several simple laboratory tests,including point-load strength,rock density,longitudinal wave velocity,Brazilian tensile strength,Schmidt hardness,and shore hardness.In this study,six data sets of indices for different rock types were utilized to predict the UCS using three nonlinear combination models,namely back propagation(BP),particle swarm optimization(PSO),and least squares support vector machine(LSSVM).Moreover,the best prediction model was examined and selected based on four performance prediction indices.The results reveal that the PSO–LSSVM model was more successful than the other two models due to its higher performance capacity.The ratios of the predicted UCS to the measured UCS for the six data sets were 0.954,0.982,0.9911,0.9956,0.9995,and 0.993,respectively.The results were more reasonable when the predicted ratio was close to a value of approximately 1.
基金supported by National Natural Science Foundation of China(Nos.81903796,81603396 and 31870338)the National Key Research and Development Program of China(No.2019YFC1711200)+2 种基金Major new drug innovation project of the ministry of science and technology(2018ZX09201011)Scientific and Technological Planning Projects of Colleges and Universities of Shandong Province(No.J18KA287)Binzhou Medical University Research Startup Fund Project(No.BY2016KYQD02)。
文摘Mentha haplocalyx(mint)is a significant traditional Chinese medicine(TCM)listed in the Catalogue of’Medicinal and Food Homology’,therefore,its geographical origins(GOs)are critical to the medicinal and food value.Laser-induced breakdown spectroscopy(LIBS)is an advanced analytical technique for GOs certification,due to the fast multi-elemental analysis requiring minimal sample pretreatment.In this study,LIBS data of sampled mint from five GOs were investigated by LIBS coupled with multivariate statistical analyzes.The spectral data was analyzed by two chemometric algorithms,i.e.principal component analysis(PCA)and least squares support vector machines(LS-SVM).Specifically,the performance of LS-SVM with least kernel and radial basis function(RBF)kernel was explored in sensitivity and robustness tests.Both LS-SVM algorithms exhibited excellent performance of classification in sensitive test and good performance(a little inferior)in robustness test.Generally,LS-SVM with linear kernel equally outperformed LS-SVM based on RBF kernel.The result indicated the potential for future applications in herbs and food,especially for in situ GOs applications of TCM authenticity rapidly.
基金funded by the National Natural Science Foundation of China(Grant No.31770769)the National Key Research and Development Program of China(No.2017YFC0504403)the Fundamental Research Funds for the Central Universities(No.2015ZCQ-GX-03).
文摘This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.
文摘The cost of highway is affected by many factors.Its composition and calculation are complicated and have great ambiguity.Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task.Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost.This work sorts out and collects 60 sets of measured data of highway engineering;establishes an expressway cost index system based on 10 factors,including main route mileage,roadbed width,roadbed earthwork,and number of bridges;and processes the data through principal component analysis(PCA)and hierarchical cluster analysis.Particle swarm optimization(PSO)is used to obtain the optimal parameter combination of the regularization parameter c and the kernel function width coefficientin least squares support vector machine(LSSVM).Results show that the average relative and mean square errors of the PCA-PSO-LSSVM model are 0.79%and 10.01%,respectively.Compared with BP neural networks and unoptimized LSSVM model,the PCA-PSO-LSSVM model has smaller relative errors,better generalization ability,and higher prediction accuracy,thereby providing a new method for highway cost prediction in complex environments.
文摘A method which adopts the combination of least squares support vector machine(LS-SVM) and Monte Carlo(MC) simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua(FLAC).The decimal ant colony algorithm(DACA) is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.