期刊文献+
共找到485篇文章
< 1 2 25 >
每页显示 20 50 100
Improved adaptive pruning algorithm for least squares support vector regression 被引量:4
1
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
Flatness intelligent control via improved least squares support vector regression algorithm 被引量:2
2
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
Fault diagnosis of power-shift steering transmission based on multiple outputs least squares support vector regression 被引量:2
3
作者 张英锋 马彪 +2 位作者 房京 张海岭 范昱珩 《Journal of Beijing Institute of Technology》 EI CAS 2011年第2期199-204,共6页
A method of multiple outputs least squares support vector regression (LS-SVR) was developed and described in detail, with the radial basis function (RBF) as the kernel function. The method was applied to predict t... A method of multiple outputs least squares support vector regression (LS-SVR) was developed and described in detail, with the radial basis function (RBF) as the kernel function. The method was applied to predict the future state of the power-shift steering transmission (PSST). A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameter σ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis. 展开更多
关键词 least squares support vector regressionls-svr fault diagnosis power-shift steering transmission (PSST)
下载PDF
A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking 被引量:2
4
作者 HU Lei YI Guoxing HUANG Chao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第1期151-162,共12页
Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a... Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance. 展开更多
关键词 least square support vector regression(LSSVR) global representative point ranking(GRPR) initial training dataset pruning strategy sparsity regression accuracy
下载PDF
Improved Scheme for Fast Approximation to Least Squares Support Vector Regression
5
作者 张宇宸 赵永平 +3 位作者 宋成俊 侯宽新 脱金奎 叶小军 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第4期413-419,共7页
The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FS... The solution of normal least squares support vector regression(LSSVR)is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR. 展开更多
关键词 support vector regression kernel method least squares SPARSENESS
下载PDF
Improved scheme to accelerate sparse least squares support vector regression
6
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.
下载PDF
Application of Least Squares Support Vector Machine for Regression to Reliability Analysis 被引量:18
7
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
原文传递
Primal least squares twin support vector regression 被引量:5
8
作者 Hua-juan HUANG Shi-fei DING Zhong-zhi SHI 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2013年第9期722-732,共11页
The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space.However,this... The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space.However,this solution is affected by time and memory constraints when dealing with large datasets.In this paper,we present a least squares version for TSVR in the primal space,termed primal least squares TSVR (PLSTSVR).By introducing the least squares method,the inequality constraints of TSVR are transformed into equality constraints.Furthermore,we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space;thus,we need only to solve two systems of linear equations instead of two QPPs.Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational time.We further investigate its validity in predicting the opening price of stock. 展开更多
关键词 Twin support vector regression Least squares method Primal space Stock prediction
原文传递
A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression 被引量:12
9
作者 ZHANG Xiu-ling, ZHANG Shao-yu, TAN Guang-zhong, ZHAO Wen-bao (Key Laboratory of Industrial Computer Control Engineering of Hebei Province, National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao 066004, Hebei, China) 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2012年第3期25-30,共6页
To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, q... To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to over- come the defects live in the existent recognition methods based on fuzzy, neural network and support vector regres- sion (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVR) was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan- cing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability. 展开更多
关键词 flatness pattern recognition least squares support vector regression cross-validation
原文传递
基于LS-SVR算法的多源监测数据高铁隧道围岩参数反分析方法 被引量:2
10
作者 李照众 畅翔宇 +1 位作者 王浩 茅建校 《Journal of Southeast University(English Edition)》 EI CAS 2023年第1期1-7,共7页
为了准确估计岩体性质,依托阳山高速铁路隧道,提出了一种基于最小二乘支持向量回归(LS-SVR)的多源监测数据高铁隧道围岩参数反分析方法.以均方根误差(RMSE)和绝对百分比误差(MAPE)为评价指标,将参数反分析结果与BP神经网络和高斯过程回... 为了准确估计岩体性质,依托阳山高速铁路隧道,提出了一种基于最小二乘支持向量回归(LS-SVR)的多源监测数据高铁隧道围岩参数反分析方法.以均方根误差(RMSE)和绝对百分比误差(MAPE)为评价指标,将参数反分析结果与BP神经网络和高斯过程回归模型结果进行比较.结果表明,对于单一类型的监测数据,考虑拱顶沉降的LS-SVR模型的RMSE和MAPE值最低.随着监测数据类型的增加,LS-SVR反分析模型的RMSE值逐渐减小,且采用拱顶沉降、收敛和仰拱隆起3种监测数据的反分析模型的RMSE值最小.相比于BP神经网络和高斯过程回归模型,LS-SVR模型具有较低的RMSE和MAPE值.相较于现有围岩力学参数反分析方法,考虑多源监测数据的LS-SVR模型具有更高的参数反分析精度. 展开更多
关键词 隧道工程 参数反分析方法 围岩力学参数 最小二乘支持向量回归算法
下载PDF
Short Term Electric Load Prediction by Incorporation of Kernel into Features Extraction Regression Technique
11
作者 Ruaa Mohamed-Rashad Ghandour Jun Li 《Smart Grid and Renewable Energy》 2017年第1期31-45,共15页
Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a rea... Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models. 展开更多
关键词 Short TERM Load PREDICTION support vector regression (SVR) KERNEL Principal Component regression (KPCR) KERNEL PARTIAL Least square regression (KPLSR)
下载PDF
试验设计及参数优化的LS-SVR显著性因子筛选
12
作者 崔庆安 崔楠 《管理科学学报》 CSCD 北大核心 2023年第12期42-61,共20页
针对最小二乘支持向量回归机(LS-SVR)应用于试验设计建模及参数优化而产生的可解释性差、难以识别显著性影响因子等不足,提出一种适用于LS-SVR的拟合不足检验及显著性因子筛选方法.首先在重复性试验设计条件下,将LS-SVR拟合模型的“残... 针对最小二乘支持向量回归机(LS-SVR)应用于试验设计建模及参数优化而产生的可解释性差、难以识别显著性影响因子等不足,提出一种适用于LS-SVR的拟合不足检验及显著性因子筛选方法.首先在重复性试验设计条件下,将LS-SVR拟合模型的“残差平方和”分解为“拟合不足平方和”与“纯误差平方和”;进而给出了“拟合不足均方”与“纯误差均方”比值的近似非中心F-分布,构造出拟合不足检验的方差分析表;在此基础上,提出一种两阶段的显著性因子筛选方法,通过考察某个因子(组合)移除后模型拟合不足显著性的变化,来推断该因子(组合)显著性.仿真研究与实证表明,所提方法不仅能够增强LS-SVR的统计可解释性,有效识别出显著性因子;而且可以得到预测性能更优的简化模型;有助于提升试验设计建模及参数优化效率,降低质量改进成本. 展开更多
关键词 因子筛选 参数优化 试验设计 拟合不足检验 最小二乘支持向量回归机
下载PDF
基于高光谱成像技术的涌泉蜜桔糖度最优检测位置 被引量:1
13
作者 李斌 万霞 +4 位作者 刘爱伦 邹吉平 卢英俊 姚迟 刘燕德 《中国光学(中英文)》 EI CAS CSCD 北大核心 2024年第1期128-139,共12页
本文旨在探索涌泉蜜桔糖度的最优检测位置和最佳预测模型,以便为蜜桔糖度检测分级提供理论依据。本文利用波长为390.2~981.3 nm的高光谱成像系统对涌泉蜜桔糖度最佳检测位置进行研究,将涌泉蜜桔的花萼、果茎、赤道和全局的光谱信息与其... 本文旨在探索涌泉蜜桔糖度的最优检测位置和最佳预测模型,以便为蜜桔糖度检测分级提供理论依据。本文利用波长为390.2~981.3 nm的高光谱成像系统对涌泉蜜桔糖度最佳检测位置进行研究,将涌泉蜜桔的花萼、果茎、赤道和全局的光谱信息与其对应部位的糖度结合,建立其预测模型。使用标准正态变量变换(SNV)、多元散射校正(MSC)、基线校准(Baseline)和SG平滑(Savitzkv-Golay)4种预处理方法对不同部位的原始光谱进行预处理,用预处理后的光谱数据建立偏最小二乘回归(PLSR)和最小二乘支持向量机(LSSVM)模型。找出蜜桔不同部位的最佳预处理方式,对经过最佳预处理后的光谱数据采用竞争性自适应重加权算法(CARS)和无信息变量消除法(UVE)进行特征波长筛选。最后,用筛选后的光谱数据建立PLSR和LSSVM模型并进行分析比较。研究结果表明,全局的MSC-CARS-LSSVM模型预测效果最佳,其预测集相关系数Rp=0.955,均方根误差RMSEP=0.395,其次是蜜桔赤道部位的SNV-PLSR模型,其预测集相关系数Rp=0.936,均方根误差RMSEP=0.37。两者预测集相关系数相近,因此可将赤道位置作为蜜桔糖度的最优检测位置。本研究表明根据蜜桔不同部位建立的糖度预测模型的预测效果有所差异,研究最优检测位置和最佳预测模型可以为蜜桔进行糖度检测分级提供理论依据。 展开更多
关键词 涌泉蜜桔 高光谱 糖度 偏最小二乘回归 最小二乘支持向量机
下载PDF
基于近红外光谱技术结合ARO-LSSVR的天麻中有效成分含量快速检测 被引量:1
14
作者 李珊珊 张付杰 +5 位作者 李丽霞 张浩 段星桅 史磊 崔秀明 李小青 《食品科学》 EI CAS CSCD 北大核心 2024年第4期207-213,共7页
为实现对天麻中天麻素和对羟基苯甲醇含量的快速、无损检测,以云南昭通乌天麻为实验对象,采集900~1 700 nm波长范围内的光谱数据。首先,采用卷积平滑和标准正态变量变换进行光谱数据预处理,其次通过竞争性自适应重加权采样法(competitiv... 为实现对天麻中天麻素和对羟基苯甲醇含量的快速、无损检测,以云南昭通乌天麻为实验对象,采集900~1 700 nm波长范围内的光谱数据。首先,采用卷积平滑和标准正态变量变换进行光谱数据预处理,其次通过竞争性自适应重加权采样法(competitive adapative reweighted sampling,CARS)与迭代保留信息变量算法进行特征波长的提取,根据基于特征波长建立最小二乘支持向量回归(least squares support vector machine,LSSVR)模型的结果,选择最佳特征波长提取方法。为了提高模型的准确率,本研究引入人工兔智能算法对LSSVR中的正则化参数γ和核函数密度σ2进行优化,并与粒子群优化算法(particle swarm optimization,PSO)、灰狼优化算法(grey wolf optimizer,GWO)进行对比,评估人工兔优化算法(artificial rabbits optimization,ARO)的优越性。结果表明,ARO算法在寻优速度、寻优能力上优于PSO、GWO;天麻素、对羟基苯甲醇的最佳预测模型均为CARS-AROLSSVR,其Rp2分别为0.969 6和0.957 7,预测均方根误差分别为0.014和0.020。综上,近红外光谱可用于天麻中有效成分的定量检测,本研究可为天麻快速检测装置的研发提供理论依据。 展开更多
关键词 近红外光谱 天麻 最小二乘支持向量回归 人工兔优化算法
下载PDF
基于LS-SVR岩石爆破块度预测 被引量:12
15
作者 史秀志 王洋 +1 位作者 黄丹 史采星 《爆破》 CSCD 北大核心 2016年第3期36-40,共5页
为了准确预测小样本条件下露天矿山岩石的爆破块度,并得到小样本条件下预测露天矿山爆破块度的有效方法,借助最小二乘支持向量机工具(LS-SVMlab)构建基于最小二乘支持向量机回归(LS-SVR)预测模型并合理优化模型参数。分别使用15组露天... 为了准确预测小样本条件下露天矿山岩石的爆破块度,并得到小样本条件下预测露天矿山爆破块度的有效方法,借助最小二乘支持向量机工具(LS-SVMlab)构建基于最小二乘支持向量机回归(LS-SVR)预测模型并合理优化模型参数。分别使用15组露天矿山爆破数据和35组爆破数据作为小样本容量和正常样本容量,对模型的预测精度进行检验。结果表明:两种样本容量下LS-SVR预测模型的预测结果精度都比同样本容量下人工神经网络(ANN)回归预测的结果精度更高,说明所提出的LS-SVR模型适用于预测露天矿山爆破块度,并且在小样本条件下更具优势。 展开更多
关键词 支持向量机 最小二乘支持向量机回归 LS-SVMlab 岩石块度 小样本预测
下载PDF
基于PSO-LSSVR的机器人磨抛材料去除模型
16
作者 蔡鸣 朱光 +2 位作者 李论 赵吉宾 王奔 《组合机床与自动化加工技术》 北大核心 2024年第1期174-177,182,共5页
为了建立磨抛工艺参数与材料去除深度的关系,建立一种基于最小二乘法支持向量回归机(LSSVR)的材料去除深度预测模型,并引入粒子群优化(PSO)算法来优化LSSVR的超参数,可提高LSSVR模型的预测准确性和全局优寻能力。搭建叶片机器人砂带磨... 为了建立磨抛工艺参数与材料去除深度的关系,建立一种基于最小二乘法支持向量回归机(LSSVR)的材料去除深度预测模型,并引入粒子群优化(PSO)算法来优化LSSVR的超参数,可提高LSSVR模型的预测准确性和全局优寻能力。搭建叶片机器人砂带磨抛实验平台,设计并进行多工艺参数实验,考虑工艺参数:砂带粒度、砂带转速、进给速度、接触力和叶片表面曲率半径,获得叶片表面的材料去除深度,最终利用实验数据建立了PSO-LSSVR叶片材料去除深度预测模型。结果表明,PSO-LSSVR模型的预测准确率为95.37%,平均预测误差为0.003463,说明PSO-LSSVR模型具有较高的预测精度,并结合实际加工情况进行实验验证可行性,证明PSO-LSSVR模型可以有效合理地建立工艺参数与材料去除深度的关系。 展开更多
关键词 机器人砂带磨抛 预测模型 工艺参数 最小二乘法支持向量回归机 粒子群算法
下载PDF
基于LS-SVR的机器人空间4DOF无标定视觉定位 被引量:7
17
作者 辛菁 刘丁 徐庆坤 《控制理论与应用》 EI CAS CSCD 北大核心 2010年第1期77-85,共9页
研究了基于智能算法的机器人无标定视觉伺服问题,提出了一种新的基于最小二乘支持向量回归的机器人无标定视觉免疫控制方法.利用最小二乘支持向量回归学习机器人位姿变化和观测到的图像特征变化之间的复杂非线性关系,其中最小二乘支持... 研究了基于智能算法的机器人无标定视觉伺服问题,提出了一种新的基于最小二乘支持向量回归的机器人无标定视觉免疫控制方法.利用最小二乘支持向量回归学习机器人位姿变化和观测到的图像特征变化之间的复杂非线性关系,其中最小二乘支持向量回归的参数由自适应免疫算法加5折交叉检验优化确定,在此基础上利用免疫控制原理设计了视觉控制器.六自由度工业机器人空间4DOF视觉定位实验结果表明了该方法的有效性. 展开更多
关键词 无标定 视觉定位 最小二乘支持向量回归 免疫控制
下载PDF
基于特征量重要度LS-SVR的WSN定位方法 被引量:5
18
作者 刘桂雄 周松斌 +1 位作者 张晓平 洪晓斌 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2008年第10期102-107,共6页
针对无线传感器网络(WSN)节点定位方法中采用粗测距技术时,节点间较大的测距误差导致定位准确度不足的问题,提出一种基于特征量重要度最小二乘支持向量回归(LS-SVR)的定位方法.该方法把未知节点到锚节点的距离作为特征量,依据... 针对无线传感器网络(WSN)节点定位方法中采用粗测距技术时,节点间较大的测距误差导致定位准确度不足的问题,提出一种基于特征量重要度最小二乘支持向量回归(LS-SVR)的定位方法.该方法把未知节点到锚节点的距离作为特征量,依据特征量的重要度进行特征提取,通过对探测区域网格化采样得到训练样本集,使用LS-SVR学习得到定位模型;在定位阶段,将未知节点的特征向量输入定位模型,利用LS—SVR良好的泛化能力实现对未知节点的准确定位.对均匀分布和C形区域随机分布的100个节点的定位实验表明,文中提出的定位方法能有效地降低测距误差对定位准确度的影响,减小平均定位误差;与采用相同测距技术的DV—Hop方法相比,均匀分布情况下该方法的平均定位误差减小7.5%~14.0%,C形区域随机分布情况下显著减小36.5%~55.2%. 展开更多
关键词 特征提取 最小二乘支持向量回归机 无线传感器网络 定位
下载PDF
可见-近红外与中红外光谱预测土壤养分的比较研究
19
作者 李学兰 李德成 +6 位作者 郑光辉 曾荣 蔡凯 高维常 潘文杰 姜超英 曾陨涛 《土壤学报》 CAS CSCD 北大核心 2024年第3期687-698,共12页
对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外(350~2500 nm)与中红外光谱(4000~650 cm^(–1))对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay(SG)平滑去噪处理,再用标准正态化(SNV)... 对土壤养分的快速和准确测定有助于适时指导施肥。为进一步研究可见-近红外(350~2500 nm)与中红外光谱(4000~650 cm^(–1))对土壤养分的预测能力,以贵州省500个土样为例,对光谱进行Savitzky-Golay(SG)平滑去噪处理,再用标准正态化(SNV)方法进行基线校正,然后分别应用偏最小二乘回归(PLSR)和支持向量机(SVM)两种方法进行建模,探讨了可见-近红外和中红外光谱对土壤全氮(TN)、全磷(TP)、全钾(TK)和碱解氮(AN)、有效磷(AP)、速效钾(AK)共六种土壤养分的预测效果。结果表明:(1)无论基于可见-近红外光谱还是中红外光谱,PLSR模型的预测精度整体均优于SVM模型。(2)中红外光谱对TN、TK和AN的预测精度均显著高于可见-近红外光谱,可见-近红外和中红外光谱均可以可靠地预测TN和TK(性能与四分位间隔距离的比率(RPIQ)大于2.10),中红外光谱可相对较可靠地预测AN(RPIQ=1.87);但两类光谱对TP、AP和AK的预测效果均较差(RPIQ<1.34)。(3)当变量投影重要性得分(VIP)大于1.5时,PLSR模型在中红外光谱区域预测TN和TK的重要波段多于可见-近红外光谱区域,TN的重要波段主要集中于可见-近红外光谱区域的1910和2207 nm附近,中红外光谱区域的1120、1000、960、910、770和668 cm^(–1)附近;TK的重要波段主要集中于可见-近红外光谱区域的540、2176、2225和2268 nm附近,中红外光谱区域的1040、960、910、776、720和668 cm^(–1)附近。因此,中红外光谱技术结合PLSR模型对土壤养分预测效果较好,可快速准确预测土壤TN和TK,可为指导适时施肥提供技术支撑。 展开更多
关键词 可见-近红外光谱 中红外光谱 土壤养分 偏最小二乘回归 支持向量机
下载PDF
基于新息的多参量混沌时间序列LS-SVR加权预测 被引量:5
20
作者 郭阳明 翟正军 姜红梅 《西北工业大学学报》 EI CAS CSCD 北大核心 2009年第1期83-87,共5页
复杂系统常常依赖于通过观测所获得的多参量混沌时间序列进行预测分析。论文借鉴单参量混沌时间序列预测的思路,考虑全部相关参量混沌时间序列中的信息,以实现多参量混沌时间序列的相空间重构。同时,基于新息优先原理和支持向量机理论,... 复杂系统常常依赖于通过观测所获得的多参量混沌时间序列进行预测分析。论文借鉴单参量混沌时间序列预测的思路,考虑全部相关参量混沌时间序列中的信息,以实现多参量混沌时间序列的相空间重构。同时,基于新息优先原理和支持向量机理论,结合混沌时间序列发展变化的规律,提出分别利用相空间重构后长期多样本和近期少样本构建2个自适应最小二乘支持向量回归预测模型进行加权预测的观点,并给出了以预测均方根误差最小为目标函数的模型参数混沌优化方法。论文以某飞机转子部件磨损故障的3个相关参量的仿真混沌时间序列为例进行了预测实验,结果表明文中方法有较好的预测精度,是一种有效的预测方法。 展开更多
关键词 支持向量机 多参量 混沌时间序列 最小二乘支持向量回归 加权预测
下载PDF
上一页 1 2 25 下一页 到第
使用帮助 返回顶部