期刊文献+
共找到180篇文章
< 1 2 9 >
每页显示 20 50 100
Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models
1
作者 Yifan Huang Zikang Zhou +1 位作者 Mingyu Li Xuedong Luo 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期3147-3165,共19页
Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were u... Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were developed.Eighty-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output parameter.As predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were selected.The normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction outcomes.According to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR model.The TSO-SVR model provides the most accurate predictions.The performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction model.The maximum charge per delay impacts the PPV prediction value the most. 展开更多
关键词 Blasting vibration metaheuristic algorithms support vector regression peak particle velocity normalized mutual information
下载PDF
Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system 被引量:1
2
作者 HAN Chi XIONG Wei +1 位作者 XIONG Minghui LIU Zhen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第6期1626-1644,共19页
As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonl... As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation. 展开更多
关键词 reconnaissance satellite system(RSS) support vector regression(SVR) gray wolf optimizer opposition-based learning parameter optimization effectiveness evaluation
下载PDF
Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors 被引量:1
3
作者 陈茜 乔连生 +2 位作者 蔡漪涟 张燕玲 李贡宇 《Chinese Journal of Chemical Physics》 SCIE CAS CSCD 2016年第5期629-634,I0002,共7页
The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accura... The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 (CYP1A2) inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biolog-ical activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identi ed to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding a n-ity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug inter-actions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy. 展开更多
关键词 Support vector machine Support vector regression Molecular docking CYPIA2 inhibitor
下载PDF
Identification of dynamic systems using support vector regression neural networks 被引量:1
4
作者 李军 刘君华 《Journal of Southeast University(English Edition)》 EI CAS 2006年第2期228-233,共6页
A novel adaptive support vector regression neural network (SVR-NN) is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is appl... A novel adaptive support vector regression neural network (SVR-NN) is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method. 展开更多
关键词 support vector regression neural network system identification robust learning algorithm ADAPTABILITY
下载PDF
Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants
5
作者 谭显胜 金晨钟 +1 位作者 李巍巍 袁哲明 《Agricultural Science & Technology》 CAS 2016年第11期2452-2456,共5页
In order to solve the problem of poor interpretability of support vector re- gression (SVR) applied in quantitative structure-property relationship (QSPR), a com- plete set of explanatory system for SVR was establ... In order to solve the problem of poor interpretability of support vector re- gression (SVR) applied in quantitative structure-property relationship (QSPR), a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR. 展开更多
关键词 Support vector regression Explanatory system SURFACTANT Significant test Quantitative structure-property relationship
下载PDF
Prediction of Pressure Drop of Slurry Flow in Pipeline by Hybrid Support Vector Regression and Genetic Algorithm Model 被引量:26
6
作者 S.K. Lahiri K.C. Ghanta 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2008年第6期841-848,共8页
This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression an... This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta-parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the lit- erature showed that the developed SVR correlation noticeably improved the prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters. 展开更多
关键词 support vector regression genetic algorithm slurry pressure drop
下载PDF
Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms 被引量:9
7
作者 Abdul-Lateef Balogun Fatemeh Rezaie +6 位作者 Quoc Bao Pham Ljubomir Gigović Siniša Drobnjak Yusuf AAina Mahdi Panahi Shamsudeen Temitope Yekeen Saro Lee 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第3期384-398,共15页
In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic informatio... In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance. 展开更多
关键词 LANDSLIDE Machine learning METAHEURISTIC Spatial modeling Support vector regression
下载PDF
Parameter selection of support vector regression based on hybrid optimization algorithm and its application 被引量:9
8
作者 Xin WANG Chunhua YANG +1 位作者 Bin QIN Weihua GUI 《控制理论与应用(英文版)》 EI 2005年第4期371-376,共6页
Choosing optimal parameters for support vector regression (SVR) is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters... Choosing optimal parameters for support vector regression (SVR) is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods, 展开更多
关键词 Support vector regression Parameters tuning Hybrid optimization Genetic algorithm(GA)
下载PDF
Small-time scale network traffic prediction based on a local support vector machine regression model 被引量:10
9
作者 孟庆芳 陈月辉 彭玉华 《Chinese Physics B》 SCIE EI CAS CSCD 2009年第6期2194-2199,共6页
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the... In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements. 展开更多
关键词 network traffic small-time scale nonlinear time series analysis support vector machine regression model
下载PDF
A Metamodeling Method Based on Support Vector Regression for Robust Optimization 被引量:5
10
作者 XIANG Guoqi HUANG Dagui 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2010年第2期242-251,共10页
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv... Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression(PR), neural networks(NN) and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression(SVR) is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm (GA). The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks(BPNN), the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure. 展开更多
关键词 support vector regression METAMODELING robust optimization genetic algorithm
下载PDF
Support vector regression modeling in recursive just-in-time learning framework for adaptive soft sensing of naphtha boiling point in crude distillation unit 被引量:4
11
作者 Venkata Vijayan S Hare Krishna Mohanta Ajaya Kumar Pani 《Petroleum Science》 SCIE CAS CSCD 2021年第4期1230-1239,共10页
Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive so... Prediction of primary quality variables in real time with adaptation capability for varying process conditions is a critical task in process industries.This article focuses on the development of non-linear adaptive soft sensors for prediction of naphtha initial boiling point(IBP)and end boiling point(EBP)in crude distillation unit.In this work,adaptive inferential sensors with linear and non-linear local models are reported based on recursive just in time learning(JITL)approach.The different types of local models designed are locally weighted regression(LWR),multiple linear regression(MLR),partial least squares regression(PLS)and support vector regression(SVR).In addition to model development,the effect of relevant dataset size on model prediction accuracy and model computation time is also investigated.Results show that the JITL model based on support vector regression with iterative single data algorithm optimization(ISDA)local model(JITL-SVR:ISDA)yielded best prediction accuracy in reasonable computation time. 展开更多
关键词 Adaptive soft sensor Just in time learning regression Support vector regression Naphtha boiling point
下载PDF
Semi-supervised Support Vector Regression Model for Remote Sensing Water Quality Retrieving 被引量:3
12
作者 WANG Xili FU Li MA Lei 《Chinese Geographical Science》 SCIE CSCD 2011年第1期57-64,共8页
This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was used for retrieving water quality variables from SPOT 5 remote sensing data. The model consi... This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was used for retrieving water quality variables from SPOT 5 remote sensing data. The model consisted of two support vector regressors (SVRs). Nonlinear relationship between water quality variables and SPOT 5 spectrum was described by the two SVRs, and semi-supervised co-training algorithm for the SVRs was es-tablished. The model was used for retrieving concentrations of four representative pollution indicators―permangan- ate index (CODmn), ammonia nitrogen (NH3-N), chemical oxygen demand (COD) and dissolved oxygen (DO) of the Weihe River in Shaanxi Province, China. The spatial distribution map for those variables over a part of the Weihe River was also produced. SVR can be used to implement any nonlinear mapping readily, and semi-supervis- ed learning can make use of both labeled and unlabeled samples. By integrating the two SVRs and using semi-supervised learning, we provide an operational method when paired samples are limited. The results show that it is much better than the multiple statistical regression method, and can provide the whole water pollution condi-tions for management fast and can be extended to hyperspectral remote sensing applications. 展开更多
关键词 semi-supervised learning support vector regression CO-TRAINING water quality retrieving model SPOT 5
下载PDF
Improved adaptive pruning algorithm for least squares support vector regression 被引量:4
13
作者 Runpeng Gao Ye San 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期438-444,共7页
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit... As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance. 展开更多
关键词 least squares support vector regression machine (LS- SVRM) PRUNING leave-one-out (LOO) error incremental learning decremental learning.
下载PDF
Cloud removal of remote sensing image based on multi-output support vector regression 被引量:3
14
作者 Gensheng Hu Xiaoqi Sun +1 位作者 Dong Liang Yingying Sun 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第6期1082-1088,共7页
Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-... Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth. 展开更多
关键词 remote sensing image cloud removal support vector regression MULTI-OUTPUT
下载PDF
Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel 被引量:2
15
作者 Saeid Shokri Mohammad Taghi Sadeghi +1 位作者 Mahdi Ahmadi Marvast Shankar Narasimhan 《Petroleum Science》 SCIE CAS CSCD 2015年第1期177-188,共12页
A novel data-driven, soft sensor based on support vector regression (SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization (HDS) process. A wid... A novel data-driven, soft sensor based on support vector regression (SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization (HDS) process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm (GA) and sequential quadratic programming (SQP) methods (GA-SQP) was developed. Performance of different optimization algorithms including GA-SQP, GA, pattern search (PS), and grid search (GS) indicated that the best average absolute relative error (AARE), squared correlation coefficient (R2), and computation time (CT) (AARE = 0.0745, R2 = 0.997 and CT = 56 s) was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization (VQ) technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy. 展开更多
关键词 Soft sensor Support vector regression Hybrid optimization method vector quantization Petroleum refinery Hydrodesulfurization process Gas oil
下载PDF
Improved IMM algorithm based on support vector regression for UAV tracking 被引量:3
16
作者 ZENG Yuan LU Wenbin +3 位作者 YU Bo TAO Shifei ZHOU Haosu CHEN Yu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第4期867-876,共10页
With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirement... With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable. 展开更多
关键词 interacting multiple model(IMM)filter constant acceleration(CA) unmanned aerial vehicle(UAV) support vector regression(SVR)
下载PDF
Flatness intelligent control via improved least squares support vector regression algorithm 被引量:2
17
作者 张秀玲 张少宇 +1 位作者 赵文保 徐腾 《Journal of Central South University》 SCIE EI CAS 2013年第3期688-695,共8页
To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm w... To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method. 展开更多
关键词 least squares support vector regression multi-output least squares support vector regression FLATNESS effective matrix predictive control
下载PDF
Prediction of dust fall concentrations in urban atmospheric environment through support vector regression 被引量:2
18
作者 焦胜 曾光明 +3 位作者 何理 黄国和 卢宏玮 高青 《Journal of Central South University》 SCIE EI CAS 2010年第2期307-315,共9页
Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study... Support vector regression (SVR) method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters (insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ) are 0.001, 0.5, and 2 000, respectively. 展开更多
关键词 support vector regression urban air quality dust fall soeio-economic factors radial basis function
下载PDF
Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA 被引量:2
19
作者 Saeid Shokri Mohammad Taghi Sadeghi +1 位作者 Mahdi Ahmadi Marvast Shankar Narasimhan 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第2期511-521,共11页
A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ... A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR. 展开更多
关键词 soft sensor support vector regression principal component analysis wavelet transform hydrodesulfurization process
下载PDF
Determination of Quality Properties of Soy Sauce by Support Vector Regression Coupled with SW-NIR Spectroscopy 被引量:2
20
作者 LIU Tong BAO Chun-fang REN Yu-lin 《Chemical Research in Chinese Universities》 SCIE CAS CSCD 2011年第3期385-391,共7页
The modern near-infrared(NIR) spectroscopy analysis is a simple, efficient and nondestructive technique, which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible... The modern near-infrared(NIR) spectroscopy analysis is a simple, efficient and nondestructive technique, which has been used in chemical analysis in diverse fields. Shortwave NIR spectroscopy is also a rapid, flexible, and cost-effective method to control product quality in food industry. The method of support vector regression coupled with shortwave NIR spectroscopy was explored for the nondestructive quantitative analysis of the important quality parameters of soy sauce, including amino nitrogen content, total acid content, salt content and color ratio. In this study, the support vector regression(SVR) models based on subtractive spectra and positive spectra were found and compared, the results show that the subtractive spectrum was more excellent than the positive spectrum. Meanwhile, R and RSE were determined, respectively, by means of original spectra and pretreated spectra[standard normal variate (SNV), first-derivative and second-derivative], and the corresponding models were successfully established. The best prediction was achieved by a support vector regression model of the first derivative transformed dataset. In addition, the result obtained by the proposed method was compared with that of Partial Least Squares(PLS), which showed that the generalization performance of the classifier based on SVR was much better than that of PLS. The results demonstrate that shortwave NIR spectroscopy combined with SVR is promising for the quality control of soy sauce. 展开更多
关键词 Shortwave near-infrared spectroscopy Support vector regression PROCESSING Soy sauce
下载PDF
上一页 1 2 9 下一页 到第
使用帮助 返回顶部