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2003-2021 全部
为您找到包含“最小二乘支持向量机”的内容共45

徐宝松,顾冲时,秦栋

2011-01-10

本文提出了一种基于提升小波和最小二支持向量的大坝变形预测方法,即通过提升小波分析提取大坝监测数据效应量,分别对各效应量使用最小二支持向量模型进行训练预测,然后将各分量的预测结果合成,作为最终

高等学校博士学科点专项科研基金(20070294023

国家自然科学基金资助项目(51079046

中央高校基本科研业务费项目(50909041

河海大学水利水电学院;河海大学 水文水资源与水利工程科学国家重点实验室,河海大学水利水电学院;河海大学 水文水资源与水利工程科学国家重点实验室,河海大学水利水电学院;河海大学 水文水资源与水利工程科学国家重点实验室

#水利工程#

He Yong,Cen Haiyan ,Bao Yidan ,Huang Min

The estimation of nitrogen status non-destructively in oilseed rape in a crop-growing period was performed using reflectance spectroscopy with least-squares support vector (LS-SVM). This study was conducted at the experiment farm of Zhejiang University, Hangzhou, China. The SPAD value was used as a reference data that reflects nitrogen status in oilseed rape. A total of 159 oilseed rape leaf samples were used for visible and near infrared reflectance spectroscopy at 325-1075 nm using a field spectroradiometer. The reflectance data processed by median filter was applied for LS-SVM regression model to predict SPAD values. The performance of LS-SVM with RBF kernel function and five input variables derived from scores of partial least squares (PLS) latent variables (LVs) was investigated. To serve this purpose, the grid-search technique using 5-fold cross-validation was used to find out the optimal values of two important parameters in LS-SVM regression model. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BPNN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD values of oilseed rape leaves. It is concluded that LS-SVM regression method is a promising technique for chemometrics in the field of quantitative prediction.

2007-12-14

教育部博士点基金(20040335034

Zhejiang University,Zhejiang University,Zhejiang University,Zhejiang University

#Agronomy#

Xu Ruirui,Bian Guoxing

In the research of the nonlinear time series prediction, we apply the least squares support vector machine (LS-SVM) to use. In order to improve the precision LS-SVM has been discussed in the following aspects. First, the parameterγand multi-step prediction capabilities of the LS-SVM network are investigated. 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.

2005-01-13

Department of Physics, Nankai University,Department of Physics, Nankai University

#Physics#

吕宁,颜鲁齐

2016-02-01

在啤酒发酵过程中,为了建立精准的传感器温度故障诊断模型,在标准支持向量机(SVM)的基础上提出了分段加权最小二支持向量的方法,该方法首先利用模糊C聚类(FCM)对样本进行聚类分析,达到划分

黑龙江省自然科学基金(F201222

哈尔滨理工大学自动化学院 ,哈尔滨,150080,哈尔滨理工大学自动化学院,哈尔滨,150080

#电子、通信与自动控制技术#

本文收录在中国科技论文在线精品论文,2016,9(10):1048-1054.

王立国,邓禄群,张晶

2009-12-30

,限制了该算法的应用。为此提出基于线性最小二支持向量的N-FINDR改进算法,该算法无需降维预处理,且采用低复杂度的距离尺度代替复杂的体积尺度来加速算法。此外还提出对野值点施加有效控制以赋予算法鲁棒性

国家自然科学基金(60802059

教育部博士点基金(200802171003

国家自然科学基金(0

哈尔滨工程大学信息与通信工程学院,哈尔滨工程大学信息与通信工程学院,哈尔滨工程大学信息与通信工程学院

#测绘科学技术#

SI Gangquan,SHI Jianquan,Guo Zhang

To solve the sparseness problem of least squares support vector machine (LSSVM) in learning process, a training algorithm of LSSVM based on active learning is investigated. In the first stage of the algorithm, in order to solve the problem of a large number of similar training data samples, we select support samples by K-means clustering method. The second stage, we obtain a model using LSSVM and conduct function estimation of the all samples, calculating the error of the estimation values and the original samples, sorting support samples and selecting the best sample. Then the selected sample is added into training set to obtain new model. And the processes are repeated until the predetermined performance requirements are achieved, thus the sparse LSSVM model is obtained. The simulation on sinc function indicates that the proposed method performs more effectively than Suykens standard sparse method for removing the redundant support vector with better sparseness and robustness. The experiments on motorcycle dataset of the UCI indicate that the proposed algorithm can solve the problem of heteroscedasticity in some degree.

2014-03-19

the Specialized Research Fund for Doctoral Program of Higher Education of China (Grant No:20130201120011

State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi Province, 710049 ,State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi Province, 710049,State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi’an, Shaanxi Province, 710049

#Mechanics#