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The seam offset identification based on support vector regression machines
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作者 曾松盛 石永华 +1 位作者 王国荣 黄国兴 《China Welding》 EI CAS 2009年第2期75-80,共6页
The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly... The principle of the support vector regression machine(SVR) is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction. 展开更多
关键词 support vector regression machine data-dependent kernel function offset identification mean filtering
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Small-time scale network traffic prediction based on a local support vector machine regression model 被引量:10
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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
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Improved adaptive pruning algorithm for least squares support vector regression 被引量:4
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作者 Runpeng Gao Ye San 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第3期438-444,共7页
As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorit... As the solutions of the least squares support vector regression machine (LS-SVRM) are not sparse, it leads to slow prediction speed and limits its applications. The defects of the ex- isting adaptive pruning algorithm for LS-SVRM are that the training speed is slow, and the generalization performance is not satis- factory, especially for large scale problems. Hence an improved algorithm is proposed. In order to accelerate the training speed, the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning. The novel objective function in the termination condition which in- volves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generali- zation performance. The effectiveness of the proposed algorithm is tested on six benchmark datasets. The sparse LS-SVRM model has a faster training speed and better generalization performance. 展开更多
关键词 least squares support vector regression machine (LS- SVRM) PRUNING leave-one-out (LOO) error incremental learning decremental learning.
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Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method 被引量:1
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作者 孙重华 江凡 《Chinese Physics B》 SCIE EI CAS CSCD 2010年第11期1-6,共6页
In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using ... In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. 展开更多
关键词 protein binding site support vector machine regression cross-validation neighbour residue
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Improved scheme to accelerate sparse least squares support vector regression
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作者 Yongping Zhao Jianguo Sun 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期312-317,共6页
The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in p... The pruning algorithms for sparse least squares support vector regression machine are common methods, and easily com- prehensible, but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications. To this end, an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine. A major advantage of this new scheme is based on the iterative methodology, which uses the previous training results instead of retraining, and its feasibility is strictly verified theoretically. Finally, experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms, and this speedup scheme is also extended to classification problem. 展开更多
关键词 least squares support vector regression machine pruning algorithm iterative methodology classification.
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Determination of reservoir induced earthquake using support vector machine and gaussian process regression
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作者 Pijush Samui Dookie Kim 《Applied Geophysics》 SCIE CSCD 2013年第2期229-234,237,共7页
The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for... The prediction of magnitude (M) of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine (SVM) and Gaussian Process Regression (GPR) for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth] (H) are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network (ANN) method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. / 展开更多
关键词 Reservoir induced earthquake earthquake magnitude support vector Machine Gaussian Process regression PREDICTION
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Application of Least Squares Support Vector Machine for Regression to Reliability Analysis 被引量:18
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作者 郭秩维 白广忱 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2009年第2期160-166,共7页
In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functiona... In order to deal with the issue of huge computational cost very well in direct numerical simulation, the traditional response surface method (RSM) as a classical regression algorithm is used to approximate a functional relationship between the state variable and basic variables in reliability design. The algorithm has treated successfully some problems of implicit performance function in reliability analysis. However, its theoretical basis of empirical risk minimization narrows its range of applications for... 展开更多
关键词 mechanism design of spacecraft support vector machine for regression least squares support vector machine for regression Monte Carlo method RELIABILITY implicit performance function
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Support vector machine regression(SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization(RRN) 被引量:1
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作者 Jing Geng Wenxia Gan +2 位作者 Jinying Xu Ruqin Yang Shuliang Wang 《Geo-Spatial Information Science》 SCIE CSCD 2020年第3期237-247,I0004,共12页
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
关键词 support vector machine regression(SVR) non-linear radiometric transforming relation Relative Radiometric Normalization(RRN) multi-source data
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X-ray image distortion correction based on SVR
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作者 袁泽慧 李世中 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2015年第3期302-306,共5页
X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image... X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression(SVR)machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one. 展开更多
关键词 X-ray image distortion correction support vector regression machine
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Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC) 被引量:6
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作者 任远 曹广益 朱新坚 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第3期458-462,共5页
Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidificat... Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO re-ceding optimization applied to the PEMFC predictive control yielded good performance. 展开更多
关键词 support vector regression Machine (SVRM) Proton Exchange Membrane Fuel Cell (PEMFC) Particle Swarm Optimization (PSO) Predictive control
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Regulatory mechanism of hormones of the pituitary-target gland axes in kidney-Yang deficiency based on a support vector machine model 被引量:8
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作者 Wang Xiufeng Zhang Lei +4 位作者 Huang Rongbo Wu Qinghua Min Jianxin Ma Na Luo Laicheng 《Journal of Traditional Chinese Medicine》 SCIE CAS CSCD 2015年第2期238-243,共6页
OBJECTIVE:To study the development mechanism of kidney-Yang deficiency through the establishment of support vector machine models of relevant hormones of the pituitary-target gland axes in rats with kidney-Yang defici... OBJECTIVE:To study the development mechanism of kidney-Yang deficiency through the establishment of support vector machine models of relevant hormones of the pituitary-target gland axes in rats with kidney-Yang deficiency syndrome.METHODS:The kidney-Yang deficiency rat model was created by intramuscular injection of hydrocortisone,and contents of the hormones of the pituita- ry-thyroid axis:thyroid stimulating hormone(TSH),3,3',5-triiodothyronine(T_3) and thyroxine(T_4);hormones of the pituitary-adrenal gland axis:adrenocorticotropic hormone(ACTH) and Cortisol(CORT);and hormones of the pituitary-gonadal axis:luteinizing hormone(LH),follicle-stimulating hormone(FSH),and testosterone(T),were determined in the early,middle,and advanced stages.Ten support vector regression(SVR) models of the hormones were established to analyze the mutual relationships among the hormones of the three axes.RESULTS:The feedback control action of the pituitary-adrenal axis began to lose efficacy from the middle stage of kidney-Kong deficiency.The contents all hormones of the three pituitary-target gland axes decreased in the advanced stage.Relative errors of the jackknife test of the SVR models all were less than 10%.CONCLUSION:Imbalances in mutual regulation among the hormones of the pituitary-target gland axes,especially loss of effectiveness of the pituitary-adrenal axis,is one pathogenesis of kidney-Yang deficiency.The SVR model can accurately reflect the complicated non-linear relationships among pituitary-target gland axes in rats with of kidney-Yang deficiency. 展开更多
关键词 Kidney Yang deficiency support vector regression machine Pituitary-thyroid axis Pituitary-adrenal axis Pituitary-gonadal axis
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Fast magnitude determination using a single seismological station record implementing machine learning techniques 被引量:4
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作者 Luis H.Ochoa Luis F.Nino Carlos A.Vargas 《Geodesy and Geodynamics》 2018年第1期34-41,共8页
In this work a Support Vector Machine Regression(SVMR) algorithm is used to calculate local magnitude(MI) using only five seconds of signal after the P wave onset of one three component seismic station. This algor... In this work a Support Vector Machine Regression(SVMR) algorithm is used to calculate local magnitude(MI) using only five seconds of signal after the P wave onset of one three component seismic station. This algorithm was trained with 863 records of historical earthquakes, where the input regression parameters were an exponential function of the waveform envelope estimated by least squares and the maximum value of the observed waveform for each component in a single station. Ten-fold cross validation was applied for a normalized polynomial kernel obtaining the mean absolute error for different exponents and complexity parameters. The local magnitude(MI) could be estimated with 0.19 units of mean absolute error. The proposed algorithm is easy to implement in hardware and may be used directly after the field seismological sensor to generate fast decisions at seismological control centers, increasing the possibility of having an effective reaction. 展开更多
关键词 Earthquake early warning support vector Machine regression Earthquake Rapid response Local magnitude Seismic event Seismology Bogota Colombia
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Performance Prediction of Carbon Fiber Protofilament Based on SAGA-SVR 被引量:1
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作者 贺聪 任立红 丁永生 《Journal of Donghua University(English Edition)》 EI CAS 2014年第2期92-97,共6页
The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based... The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately. 展开更多
关键词 support vector regression (SVR) machine genetic algorithm( GA simulated annealing algorithm SA carbon fiber perforrmance prediction
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Transient reliability optimization for turbine disk radial deformation
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作者 费成巍 白广忱 +2 位作者 唐文忠 蔡逸思 高海峰 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第2期344-352,共9页
The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous opera... The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method(ERSM), including ERSM-based quadratic function(QF-ERSM) and ERSM-based support vector machine of regression(SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory. 展开更多
关键词 turbine disk radial deformation reliability-based transient design optimization extremum response surface method support vector machine regression
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Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning
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作者 Mohammed A.Aleid Khaled A.Z.Alyamani +3 位作者 Mohieddine Rahmouni Theyazn H.H.Aldhyani Nizar Alsharif Mohammed Y.Alzahrani 《Computers, Materials & Continua》 SCIE EI 2021年第5期2029-2047,共19页
This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a question... This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents.In other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new pandemic.To focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)algorithms.According to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and anxiety.The health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age factors.The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period.The results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety. 展开更多
关键词 COVID-19 health habits safety behaviors ANXIETY support vector machine regression regression trees
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Intelligent Forecasting of Sintered Ore’s Chemical Components Based on SVM
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作者 钟珞 王清波 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2011年第3期583-587,共5页
Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing p... Using object mathematical model of traditional control theory can not solve the forecasting problem of the chemical components of sintered ore.In order to control complicated chemical components in the manufacturing process of sintered ore,some key techniques for intelligent forecasting of the chemical components of sintered ore are studied in this paper.A new intelligent forecasting system based on SVM is proposed and realized.The results show that the accuracy of predictive value of every component is more than 90%.The application of our system in related companies is for more than one year and has shown satisfactory results. 展开更多
关键词 sintered ore support vector machine intelligent forecasting nonlinear regression optimized control
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Forecasting stock closing prices with an application to airline company data
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作者 Xu Xu Yixiang Zhang +2 位作者 Clare Anne McGrory Jinran Wu You-Gan Wang 《Data Science and Management》 2023年第4期239-246,共8页
Forecasting stock market movements is a challenging task from the practitioners’point of view.We explore how model selection via the least absolute shrinkage and selection operator(LASSO)approach can be better used t... Forecasting stock market movements is a challenging task from the practitioners’point of view.We explore how model selection via the least absolute shrinkage and selection operator(LASSO)approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines.Combining the LASSO method with multiple external data sources in our model leads to a robust and efficient method to predict stock behavior.We also compare our approach with ridge,tree,and support vector machine regressions,as well as neural network approaches to model the data.We include lags of each external variable and response variable in the model,resulting in a total of 870 predictor variables.The empirical results indicate that the LASSO-fitted model is the most effective when compared to other approaches we consider.The results show that the closing price of an airline stock is affected by its closing price for the previous days and those of other types of airlines and is significantly correlated with the Shanghai Composite Index for the previous day and 3 days prior.Other influencing factors include the positive impact of the Shanghai Composite Index daily share volume,the negative impact of loan interest rates,the amount of highway passenger and railway freight turnover,etc. 展开更多
关键词 Chinese airlines LASSO Ridge regression support vector machine regression Forecasting
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Pedotransfer functions for predicting bulk density of coastal soils in East China 被引量:1
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作者 Guanghui ZHENG Caixia JIAO +4 位作者 Xianli XIE Xuefeng CUI Gang SHANG Chengyi ZHAO Rong ZENG 《Pedosphere》 SCIE CAS CSCD 2023年第6期849-856,共8页
Soil bulk density(BD) is an important physical property and an essential factor for weight-to-volume conversion. However, BD is often missing from soil databases because its direct measurement is labor-intensive, time... Soil bulk density(BD) is an important physical property and an essential factor for weight-to-volume conversion. However, BD is often missing from soil databases because its direct measurement is labor-intensive, time-consuming, and sometimes impractical, particularly on a large scale. Therefore, pedotransfer functions(PTFs) have been developed over several decades to predict BD. Here, six previously revised PTFs(including five basic functions and stepwise multiple linear regression(SMLR)) and two new PTFs, partial least squares regression(PLSR) and support vector machine regression(SVMR), were used to develop BD-predicting PTFs for coastal soils in East China. Predictor variables included soil organic carbon(SOC) and particle size distribution(PSD). To compare the robustness and reliability of the PTFs used, the calibration and prediction processes were performed 1 000 times using the calibration and validation sets divided by a random sampling algorithm. The results showed that SOC was the most important predictor, and the revised PTFs performed reasonably although only SOC was included. The PSD data were useful for a better prediction of BD, and sand and clay fractions were the second and third most important properties for predicting BD. Compared to the other PTFs, the PLSR was shown to be slightly better for the study area(the average adjusted coefficient of determination for prediction was 0.581). These results suggest that PLSR with SOC and PSD data can be used to fill in the missing BD data in coastal soil databases and provide important information to estimate coastal carbon storage, which will further improve our understanding of sea-land interactions under the conditions of ongoing global warming. 展开更多
关键词 partial least squares regression particle size distribution soil organic carbon stepwise multiple linear regression support vector machine regression
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Spectroscopy-Based Soil Organic Matter Estimation in Brown Forest Soil Areas of the Shandong Peninsula, China 被引量:3
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作者 GAO Lulu ZHU Xicun +3 位作者 HAN Zhaoying WANG Ling ZHAO Gengxing JIANG Yuanmao 《Pedosphere》 SCIE CAS CSCD 2019年第6期810-818,共9页
Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In thi... Soil organic matter (SOM) is important for plant growth and production. Conventional analyses of SOM are expensive and time consuming. Hyperspectral remote sensing is an alternative approach for SOM estimation. In this study, the diffuse reflectance spectra of soil samples from Qixia City, the Shandong Peninsula, China, were measured with an ASD FieldSpec 3 portable object spectrometer (Analytical Spectral Devices Inc., Boulder, USA). Raw spectral reflectance data were transformed using four methods: nine points weighted moving average (NWMA), NWMA with first derivative (NWMA + FD), NWMA with standard normal variate (NWMA + SNV), and NWMA with min-max standardization (NWMA + MS). These data were analyzed and correlated with SOM content. The evaluation model was established using support vector machine regression (SVM) with sensitive wavelengths. The results showed that NWMA + FD was the best of the four pretreatment methods. The sensitive wavelengths based on NWMA + FD were 917, 991, 1 007, 1 996, and 2 267 nm. The SVM model established with the above-mentioned five sensitive wavelengths was significant ( R 2 = 0.875, root mean square error (RMSE) = 0.107 g kg −1 for calibration set;R 2 = 0.853, RMSE = 0.097 g kg −1 for validation set). The results indicate that hyperspectral remote sensing can quickly and accurately predict SOM content in the brown forest soil areas of the Shandong Peninsula. This is a novel approach for rapid monitoring and accurate diagnosis of brown forest soil nutrients. 展开更多
关键词 Brown forest soil Hyperspectral remote sensing Nine points weighted moving average Standard normal variate Sensitive wavelength Spectral reflectance support vector machine regression
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An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms 被引量:5
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作者 Wang Jiankang Zhang Haibo +2 位作者 Yan Changkai Duan Shujing Huang Xianghua 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2013年第1期94-103,共10页
In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection (PS) algorithm and multi-input multi-output recursive reduced least square support ve... In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection (PS) algorithm and multi-input multi-output recursive reduced least square support vector regression (MRR-LSSVR) machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression (LSSVR) machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5&. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method. 展开更多
关键词 Adaptive engine model Least square support vector regression machine Modeling method Parameter selection Turbo-shaft engine
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