The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for...By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. ...In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.展开更多
Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the...Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.展开更多
A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, ...A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.展开更多
In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization...In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.展开更多
The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on...The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptors were calculated for each compounds using dragon package. The variable selection technique of enhanced replacement method(ERM) was employed to select optimal subset of descriptors. Our investigation reveals that the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact and that ERM method is unable to model the solvation enthalpy accurately. The standard error value of prediction set for support vector machine(SVM) is 1.681 kJ ? mol^(-1) while it is 4.624 kJ ? mol^(-1) for ERM. The results established that the calculated ΔHSolvvalues by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by ERM one. This indicates that SVM can be used as an alternative modeling tool for QSPR studies.展开更多
To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector mac...To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.展开更多
Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very la...Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v...Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.展开更多
Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel...Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.展开更多
基于视觉的手势识别中,手势的识别效果易受手势旋转,光照亮度的影响,针对该问题,借鉴了目标识别和图像检索领域的Bag of Features(特征袋)算法,将Bag of Features算法应用到手势识别领域。通过SURF(加速鲁棒性特征)算法提取手势图像的...基于视觉的手势识别中,手势的识别效果易受手势旋转,光照亮度的影响,针对该问题,借鉴了目标识别和图像检索领域的Bag of Features(特征袋)算法,将Bag of Features算法应用到手势识别领域。通过SURF(加速鲁棒性特征)算法提取手势图像的特征描述符,使手势对尺度、旋转、光照具有很强的适应力,再应用Bag of Features算法把SURF特征描述符映射到一个统一维度的向量,即Bag of Features特征向量,再用支持向量机对图像得到的特征向量进行训练分类。实验结果表示,该方法不仅具有较高的时间效率,满足手势识别的实时性,而且即使在很大角度的旋转以及亮度的变化下,仍能达到较高的识别率。展开更多
To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, ...To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate 32 groups of training samples. Least square support vector machine(LS-SVM) was applied for surrogate model, and the optimal design parameters were determined by a kind of chaotic optimization algorithm. As hole length, lateral expansion angle and forward expansion angle are 90 mm, 20° and 5°, the area-averaged film cooling effectiveness can reach its maximum value in the design space. LS-SVM coupled with chaotic optimization algorithm is a promising scheme for the optimization of shaped film cooling holes.展开更多
According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flo...According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment.展开更多
Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructe...Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.展开更多
A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the r...A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.展开更多
The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then t...The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.展开更多
Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金Project(50878082) supported by the National Natural Science Foundation of ChinaProject(200631880237) supported by the Science and Technology Program of West Transportation of the Ministry of Transportation of ChinaKey Project(09JJ3104) supported by the Natural Science Foundation of Hunan Province, China
文摘In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.
基金the National Natural Science Foundation of China (60574075)
文摘Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.
基金supported by Project of the National Natural Science Foundation of China(Grant No.62073256)in part by Shaanxi Provincial Science and Technology Department(Grant No.2020GY-125)。
文摘In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.
文摘The purpose of this paper is to present a novel way to building quantitative structure-property relationship(QSPR) models for predicting the gas-to-benzene solvation enthalpy(ΔHSolv) of 158 organic compounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptors were calculated for each compounds using dragon package. The variable selection technique of enhanced replacement method(ERM) was employed to select optimal subset of descriptors. Our investigation reveals that the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact and that ERM method is unable to model the solvation enthalpy accurately. The standard error value of prediction set for support vector machine(SVM) is 1.681 kJ ? mol^(-1) while it is 4.624 kJ ? mol^(-1) for ERM. The results established that the calculated ΔHSolvvalues by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by ERM one. This indicates that SVM can be used as an alternative modeling tool for QSPR studies.
基金Project(51335003)supported by the National Natural Science Foundation of ChinaProject(20111102110011)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.
文摘Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
基金Projects(2013BAB02B01,2013BAB02B03)supported by the National Key Technologies R&D Program of ChinaProjects(41072224,41272347)supported by the National Natural Science Foundation of China
文摘Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.
基金Sponsored by the National Natural Science Foundation of China(51006052)
文摘Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
文摘基于视觉的手势识别中,手势的识别效果易受手势旋转,光照亮度的影响,针对该问题,借鉴了目标识别和图像检索领域的Bag of Features(特征袋)算法,将Bag of Features算法应用到手势识别领域。通过SURF(加速鲁棒性特征)算法提取手势图像的特征描述符,使手势对尺度、旋转、光照具有很强的适应力,再应用Bag of Features算法把SURF特征描述符映射到一个统一维度的向量,即Bag of Features特征向量,再用支持向量机对图像得到的特征向量进行训练分类。实验结果表示,该方法不仅具有较高的时间效率,满足手势识别的实时性,而且即使在很大角度的旋转以及亮度的变化下,仍能达到较高的识别率。
基金Project(U1508212)supported by the National Natural Science Foundation of ChinaProject(2015M570448)supported by the Postdoctoral Science Foundation of China
文摘To improve the cooling performance, shape optimization of a laidback fan-shaped film cooling hole was performed. Three geometric parameters, including hole length, lateral expansion angle and forward expansion angle, were selected as the design parameters. Numerical model of the film cooling system was established, validated, and used to generate 32 groups of training samples. Least square support vector machine(LS-SVM) was applied for surrogate model, and the optimal design parameters were determined by a kind of chaotic optimization algorithm. As hole length, lateral expansion angle and forward expansion angle are 90 mm, 20° and 5°, the area-averaged film cooling effectiveness can reach its maximum value in the design space. LS-SVM coupled with chaotic optimization algorithm is a promising scheme for the optimization of shaped film cooling holes.
基金Project(50911130366) supported by the National Natural Science Foundation of China
文摘According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment.
文摘Support vector machines （SVMs） are initially designed for binary classification. How to effectively extend them for multiclass classification is still an ongoing research topic. A multiclass classifier is constructed by combining SVM^light algorithm with directed acyclic graph SVM （DAGSVM） method, named DAGSVM^light A new method is proposed to select the working set which is identical to the working set selected by SVM^light approach. Experimental results indicate DAGSVM^light is competitive with DAGSMO. It is more suitable for practice use. It may be an especially useful tool for large-scale multiclass classification problems and lead to more widespread use of SVMs in the engineering community due to its good performance.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘A support vector machine time series forecasting model based on rough set data preprocessing was proposed by combining rough set attribute reduction and support vector machine regression algorithm. First, remove the redundant attribute for forecasting from condition attribute by rough set method; then use the minimum condition attribute set obtained after the reduction and the corresponding initial data, reform a new training sample set which only retain the important attributes influencing the forecasting accuracy; study and train the support vector machine with the training sample obtained after reduction, and then input the reformed testing sample set according to the minimum condition attribute and corresponding initial data. The model was tested and the mapping relation was got between the condition attribute and forecasting variable. Eventually, power supply and demand were forecasted in this model. The average absolute error rates of power consumption of the whole society and yearly maximum load are respectively 14.21% and 13.23%. It shows that RS-SVM time series forecasting model has high forecasting accuracy.
基金the National Natural Science Foundation of China (60503024, 50634010)
文摘The method to compress the training dataset of Support Vector Machine （SVM） based on the character of the Support Vector Machine is proposed. First, the distance between the unit in two training datasets, and then the samples that keep away from hyper-plane are discarded in order to compress the training dataset. The time spent in training SVM with the training dataset compressed by the method is shortened obviously. The result of the experiment shows that the algorithm is effective.
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
