Description: support vector machine （支持向量机）方法是目前分类方法中比较好的一个分类方法，实验证明准确度非常高！-support vector machine (support vector machine) method is a better classification of a classification method, experimental proof of a very high accuracy! Platform: |
Size: 2048 |
Author:李萍 |
Hits:
Description: 用遗传算法( Genetic Algorithm，GA) 搜寻可识别被不同农药污染脐橙的可见/近红外光谱的最佳特征光
谱区间及波长，并建立了支持向量机( Support Vector Machines，SVM) 定性分析模型。实验供试农药为灭多威、
氰戊菊酯和氧乐果3 种。通过GA 来搜寻整个波段范围( 460 ～ 1 800 nm) ，将得到的9 个最佳特征光谱区间所
包含的波长( 共318 个) 作为SVM 建模的输入变量，对识别被3 种农药污染脐橙的准确率为100 。并继续应
用GA 优化，得到71 个特征波长，此时建立的SVM 模型的识别准确率为99． 57 。虽然识别的准确率有所下降，但是模型的复杂程度得到了很大的优化，其输入变量减少到71 个。实验结果表明利用可见/近红外光谱技
术结合SVM 方法可以有效识别被不同农药污染的脐橙。-Genetic algorithm ( GA) was used to search for the best characteristic spectral ranges andwavelengths of visible /near － infrared spectra ( Vis /NIRs) ，a qualitative analysis model of support vector machine
( SVM) was set up to recognize navel oranges contaminated with different pesticides． The pesticides in
the experiment were Methomyl，fenvalerate and omethoate． Using GA to search the entire band range ( 460 ～1 800 nm) ，the 9 best characteristic spectral ranges ( 318 wavelengths) were used as the input variables of SVM model and the accuracy of the prediction set classification was 100 ． Then GA method was used continually
and 71 wavelengths were extracted，the corresponding SVM model was built with 99． 57 accuracy． Although
the classification accuracy rate declined，the complexity of the model was greatly optimized by reducing
the input variables to 71． The experiment results showed that the application of Vis /NIRs combined with SVM can effectively detect the navel oranges conta Platform: |
Size: 220160 |
Author:ygliang30 |
Hits:
Description: 嵌入维数自适应最小二乘支持向量机
状态时间序列预测方法
Condition Time Series Prediction Using Least Squares Support Vector Machine
with Adaptive Embedding Dimension
针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应
最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv Platform: |
Size: 342016 |
Author: |
Hits:
Description: 针靖目前人脸检测方法遵度与精度难以兼有的问题。本文提出了一种嬉合离斯横甓和党持向量机的人膝检测方法。
先载用皮肤颜色在YCbCr窒阍鲢聚类性。蛄肤色建立高斯模型以分制虫可能的人脸菇域。露将迭臻区域输入捌支特向量机
检测并标记出检攫ll结暴!实验结暴证瞬。本文提出的方法捡溅效装令人满意。-Needle Jing compliance degree of accuracy of the face detection method is difficult to both problems. This paper presents a play co-the knee detection method from the people of Sri Lanka cross Pi and the party held vector machine. First set out with skin color clustering of in YCbCr smothering steel silver carp. The Squilla color to create a Gaussian model insects may face a points system mushroom domain. Exposed Diego Pegasus regions to input Ba support special vector machine to detect and mark the seized grab ll junction storm! The experimental junction storm certificate instantaneous. In this paper, the method of picking the splash effect fitted satisfactorily. Platform: |
Size: 362496 |
Author:东方 |
Hits:
Description: 非线性控制系统的支持向量机辨识建模研究
针对非线性控制系统辨识建模难的问题， 系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法，
然后利用回归型支持向量机( Support Vector Regression， SVR) 设计了一个非线性控制系统的辨识建模系统 仿真试验结果表明， SVR 具有很高的建模精度和较强的泛化能力， 从而验证了该辨识方法的有效性和先进性。-Nonlinear Control Systems Support Vector Machine Identification Modeling modeling for nonlinear control system identification difficult problem, the system studied based on support vector machine identification modeling nonlinear control systems theory and method, and then use the support vector regression machines (Support Vector Regression, SVR) designed a nonlinear control system identification modeling system simulation results showed that, SVR modeling with high accuracy and generalization ability, in order to verify the validity of the identification method and advanced. Platform: |
Size: 390144 |
Author:东土大唐 |
Hits:
Description: This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are particle swarm optimization algorithm (PSO) and genetic
algorithm optimization (GA). These two optimization algorothms
are used for efficiently and effectively determine the optimal
parameters for SVM. The resulatant two hybrid algorithms, i.e.
SVM-PSO and SVM-GA can improve the performances of the
original SVM algorithm on classifying the incipient faults in
power transformers. Extensive case studies and statistic
comparison among the original SVM, SVM-PSO, and SVM-GA
over multiple datasets are also provided. Calculation results may
demonstrate the effectiveness and applicability of the two hybrid
algorithms in improving the classification accuracy of SVM for
condition assessment of power transformer. Platform: |
Size: 985088 |
Author:pse |
Hits:
Description: This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are particle swarm optimization algorithm (PSO) and genetic
algorithm optimization (GA). These two optimization algorothms
are used for efficiently and effectively determine the optimal
parameters for SVM. The resulatant two hybrid algorithms, i.e.
SVM-PSO and SVM-GA can improve the performances of the
original SVM algorithm on classifying the incipient faults in
power transformers. Extensive case studies and statistic
comparison among the original SVM, SVM-PSO, and SVM-GA
over multiple datasets are also provided. Calculation results may
demonstrate the effectiveness and applicability of the two hybrid
algorithms in improving the classification accuracy of SVM for
condition Platform: |
Size: 670720 |
Author:pse |
Hits:
Description: 基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。
-Based on least squares support vector machine theory, combined with a wind farm measured wind speed data, the establishment of a wind vector machine forecasting model of least squares support. The velocity of the wind farm were predicted in advance 1h, the mean absolute percentage error of only 8.55 of its forecast, forecast effect is ideal. While the text of the wind speed forecasting models and neural networks, support vector machines (support vector machine, SVM) wind speed prediction models were compared with established theories. Simulation results show that our proposed model on prediction accuracy and computing speed are superior to other models. Platform: |
Size: 862208 |
Author:杰 |
Hits:
Description: 遗传算法优化支持向量机的惩罚系数C与高斯核系数g，能够提高支持向量机的分类精度-Genetic algorithm to optimize the punish coefficient of support vector machine (SVM) with gaussian kernel coefficient C g, can improve the support vector machine (SVM) classification accuracy Platform: |
Size: 2048 |
Author:T和 |
Hits:
Description: Diabetic retinopathy is an important branch of ophthalmology. Non - proliferative diabetic retinopathy is used to detect Microaneurysms in the early stage. Microaneurysms are verified through fundus images; where in the fine red-dots near the blood vessels confirm this defect. Conventional methods and their weak resolution seldom can identify to such accuracies. In this work, we present a procedure to identify Microaneurysms with higher accuracy. The retinal vessels are extracted, from collected fundus image, using a Gabor wavelet which delivers high accuracy output. For accurate analysis the image it is sub divided into two regions, neighborhood and non-vessel neighborhood for expediting support vector machine (SVM) analysis. Further the SVM engine is trained for positive and negative samples of identified region fundus images. Then by sliding window technique, the entire test image is analyzed limiting analysis by SVM engine for near vessel region. This improves overall performance of the analysis and permits time available for a deeper/ sensitivity analysis of near vessel areas. The logic and the code has been tested on sample images and the results have been satisfactory. Platform: |
Size: 561690 |
Author:praneethtm@gmail.com |
Hits:
Search - support vector machine with accuracy