网站首页

登录

Ordered-neighborhood-rough-set

于 2017-03-04 发布文件大小:416KB

 0  220

下载积分: 1 下载次数: 21

我要下载

代码说明：

利用基于排序的邻域粗糙集算法对具有高维属性的元数据进行属性约简，删减多余的无关属性，避免模型的过拟合，提高模型预测精度和模型运算速度(The algorithm based on the ordered neighborhood rough set is used to reduce the attributes of the high dimension attributes, to eliminate the redundant irrelevant attributes, to avoid the over fitting of the model, and to improve the accuracy of the model prediction and the operation speed of the model)

文件列表：

基于排序的邻域粗糙集
....................\clsf_dpd_fast.asv,1621,2016-06-01
....................\clsf_dpd_fast.m,1610,2016-06-03
....................\featureselect_fast.m,1808,2016-06-03
....................\init.m,725,2016-06-03
....................\ionosphere_stand.mat,58274,2016-05-19
....................\ployinterp_column.m,1866,2015-04-15
....................\RBF_4.2.xml,289728,2016-05-20
....................\RBF_4.3.xml,18893,2016-05-20
....................\sonar_stand.mat,78687,2016-05-19
....................\soy_stand.mat,632,2016-05-19
....................\Standard.m,133,2016-06-03
....................\telco_sample_cut.mat,31012,2016-05-22
....................\telco_sample_normali.mat,40462,2016-05-14
....................\telco_sample_stand.txt,97811,2016-05-22
....................\wdbc_stand.mat,118486,2016-05-19
....................\wine_stand.mat,9338,2016-05-19
....................\wpbc_stand.mat,43435,2016-05-19

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

用灰色GM(1,1)模型对陕西省GDP 做预测
本文先用灰色GM(1,1)模型对陕西省GDP 做预测, 经过检验, 再用此模型确定另外一些未知指标, 最后通过确定的这些指标用灰色GM(1,N)模型作预测, 通过比较总结两种模型各自的特点, 从而科学的指导陕西省经济发展,国内生产总值预测模型具有重要的现实意义。 (This article first gray GM (1,1) models do forecast GDP of Shaanxi province, on examination, then this model to determine other unknown targets, and finally through the identification of these indicators of gray GM (1, N) model for prediction, by Comparison of two models summarize the characteristics of their own, so the guidance of scientific economic development in Shaanxi Province, China' s GDP forecast model has important practical significance.)

2010-08-28 22:08:24下载

积分：1
GPC_Iden_C
广义预测控制里，对象参数未知条件下的GPC自适应算法(Generalized predictive control, the object under unknown parameters of GPC adaptive algorithm)

2011-10-23 01:32:18下载

积分：1
bilateralfilter
基于双边滤波的高动态图像显示，把10位的图像显示在8位的显示器上( High dynamic image display based on bilateral filtering)

2015-04-07 14:05:52下载

积分：1
capture-daq
convolution code, changes over time, pulse pwm, capture sound port compared matlab matlab own

2013-08-03 02:28:26下载

积分：1
2010-mathematical-modeling
2010年全国大学生数学建模赛题程序，储油罐的变位识别与罐容表标定的等效模型。(2010 program of the National Mathematical Modeling Contest, storage tanks deformation equivalent model identification and calibration of the tank capacity table.)

2012-05-24 01:27:37下载

积分：1
ID3+C4.5
ID3+C4.5的源程序。用于数据挖掘决策算法的一个实例。(ID3 C4.5 of the source. Data Mining for a decision algorithm examples.)

2007-06-07 15:05:12下载

积分：1
daoying
利用一个彩色图片，利用matlab编程编成一个有可动倒影的图像(The movable reflection )

2013-11-14 10:18:11下载

积分：1
ExperimentReport2
说明：《信号与系统》计算机练习－利用matlab 西安交通大学出版社实验二("Signals and systems" computer exercises-using Matlab Xi'an Jiaotong University Press Experiment 2)

2006-04-13 18:59:09下载

积分：1
AR_model
AR模型参数估计：已知某系统的输出信号，试估计输入白噪声的功率及AR模型的参数(estimation of parameter for AR model)

2012-04-06 13:52:42下载

积分：1
针对风电场短期风速的预测提出一种基于小波变换的组合预测方法 Wind-Speed-Combined-Prediction
针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构，得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性，对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测，对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。(In order to improve short-term wind speed prediction accuracy of wind farms，a combined prediction method based on the wavelet transform is proposed. Firstly，the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm. The approximation components and the detail components of the short-term wind speed are then obtained. Next，according to the different characteristics of these components，the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictivemodels for the approximate components and the detail components respectively. Then，the final predictive value of the short-term wind speed is obtained by the combination of the two components. The simulation results indicate that higher accuracy can be obtained in this prediction method.)

2016-12-23 10:12:27下载

积分：1

696518资源总数
106208会员总数
21今日下载