Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating ...Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.展开更多
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona...In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...展开更多
[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored...[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by...Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.展开更多
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying result...In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.展开更多
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
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
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.展开更多
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
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
In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong cou...In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm.The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness.And application results show that the relative errors of the soft measurement model are less than 3.34%.展开更多
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet...Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.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
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (...In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.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
As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a mult...As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.展开更多
Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimat...Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control of a general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is un- known or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.展开更多
The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we e...The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.展开更多
基金This research was funded by the National Natural Science Fund of China[grant number 41701415]Science fund project of Wuhan Institute of Technology[grant number K201724]Science and Technology Development Funds Project of Department of Transportation of Hubei Province[grant number 201900001].
文摘Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance.
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
基金National High-tech Research and Development Pro-gram (2006AA04Z405)
文摘In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for...
基金Supported by the National Natural Science Foundation of China(31101085)the Program for Young Core Teachers of Colleges in Henan(2011GGJS-094)the Scientific Research Project for the High Level Talents,North China University of Water Conservancy and Hydroelectric Power~~
文摘[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
文摘Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312200) and the Center for Bioinformatics Pro-gram Grant of Harvard Center of Neurodegeneration and Repair,Harvard Medical School, Harvard University, Boston, USA
文摘In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.
基金Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘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.
文摘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.
基金Supported by the State Key Development Program for Basic Research of China （No.2002CB312200） and the National Natural Science Foundation of China （No.60574019）.
文摘Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
基金supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT。
文摘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.
文摘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,
基金Project(51176045)supported by the National Natural Science Foundation of ChinaProject(2011ZK2032)supported by the Major Soft Science Program of Science and Technology Ministry of Hunan Province,China
文摘In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological & management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine(FLS-SVM) soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm.The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness.And application results show that the relative errors of the soft measurement model are less than 3.34%.
基金supported by the National Natural Science Foundation Project of China(Nos.72088101 and 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author was funded by China Scholarship Council(No.202106370038).
文摘Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.
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.
文摘In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.
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the National Natural Science Foundation of China(No.60905066)the Natural Science Foundation of Chongqing(No.cstc2018jcyjA0667)
文摘As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
基金Project supported by the National Basic Research Program (973) of China (No. 2002CB312200), and the Hi-Tech Research and Devel-opment Program (863) of China (No. 2002AA412010)
文摘Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control of a general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is un- known or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.
文摘The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.
