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Description: GM(1,1)的程序，要用就下啥，可以预测的，里面有详细的说明，很容易看懂的！-GM (1,1) procedures, to use on the next receivers, and can be predicted, there are detailed explanations, it is easy to read!
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Size: 2048 |
Author: dc |
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Description: 文件为灰色建模的Matlab程序，供大家参考学习，希望互相交流，共同进步。-documents gray Matlab modeling procedures for reference learning, hope mutual exchanges and common progress.
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Size: 1024 |
Author: 张晓伟 |
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Description: 用MATLAB实现GMSK信号的产生与解调，希望给大家带来帮助！-MATLAB GMSK signal demodulation with the selection, we hope to bring help!
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Size: 279552 |
Author: 李同康 |
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Description: GM(1,1)模型1-4
1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。
2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。
3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。
4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。
gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。
gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。
gm3比gm1模拟精度要高。
可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。
所有程序在matlab6.0上调试通过。-GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu
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Author: wangyi |
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Description: 关于灰色系统预测GM(1,1)模型在matlab6.5中的实现-Prediction on the gray system GM (1,1) model in the matlab6.5 realize
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Size: 1024 |
Author: 柴潇 |
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Description: 一个GM(1,1)模型的程序,添加到matlab目录中即可直接调用.附有一个关于灰色模型的简述文档,可对GM(1,1)原理有一个初始的了解.-A GM (1,1) model of the process, added to the matlab directory can be called directly. Accompanied by a brief description of the gray model on the document, can GM (1,1) principle have an initial understanding.
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Size: 19456 |
Author: zmx8901 |
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Description: 这是MATLAB 图形用户界面的一个例子
欢迎大家一起交流学习心得 我也是刚刚接触MATLAB-This is a MATLAB graphical user interface with an example welcome everyone to exchange learning experiences I have just contacted MATLAB
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Author: gming |
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Description:
function GetParam(Vx,Vy)
global gh
global gf
global gu
global gr
global gd
global gff
global gM
％global gn -function GetParam (Vx, Vy) global gh global gf global gu global gr global gd global gff global gM global gn
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Author: |
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Description: ％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％- EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates
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Size: 3072 |
Author: Shaoqing Yu |
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Description: 灰色模型GM(1,1)原始数列经过光滑优化-Gray model GM (1,1) with smooth Optimized original series
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Size: 1024 |
Author: 小城 |
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Description: 基于matlab的gmsk信号的调制，采用查表算法-Matlab-based signal of GMSK modulation, the use of look-up table algorithm
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Size: 1236992 |
Author: maqiang |
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Description: 基于灰色GM(1,1)理论的电力系统负荷预测程序
-Based on Gray GM (1,1) theory of power system load forecasting procedures
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Size: 2048 |
Author: liu |
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Description: 一种简单的灰色预测实现的方法，data为需要输入的一维数组，N为需要预测的值的个数-Gm(1,1) matlab code ,input array data,input number N return array with N prediction value.
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Size: 1024 |
Author: 林瑞蓝 |
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Description: GM Science GM模型 全称：灰色理论的微分方程型模型 灰色理论的微分方程型模型称为GM模型，G表示grey(灰)，M表示Model(模型)．GM(1，N)表示1阶的，N个变量的微分方程型模型．而GM(1，1)则是1阶的， 1个变量的微分方程型模型。-GM Science GM model name: the theory of differential equations gray gray theory-based model of the differential equation-based model known as the GM model, G said grey (gray), M said the Model (model). GM (1, N), said a band, N-type model of a variable differential equations. The GM (1,1) is an order, and a variable differential equation-based model.
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Size: 1024 |
Author: 赵 |
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Description: 灰色预测的ＭＡＴＬＡＢ程序，只要你将输入向量换成你的数据，结果、检验都出来了-Gray forecast MATLAB program, as long as you replace the input vector of your data, the results, testing all out. . .
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Size: 1024 |
Author: xufeng |
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Description: MATLAB编写的灰色预测模型源代码 可以根据自己的实际进行修改 -Gray prediction model written in MATLAB source code can be modified according to their actual
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Size: 3072 |
Author: chenbohu |
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Description: 灰色预测模型,简单实用，可用于经济预测及其他预测问题，预测精度高-Gray prediction model , simple and practical, can be used for economic forecasts and other prediction problem, prediction accuracy
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Size: 4096 |
Author: 梅雨 |
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Description: 灰色理论模型主要采用的是的matlab编程，直接带入数据变成得出结果。-matlab GM(1,N)
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Author: 刘恒宇 |
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Description: GM(1,1)灰色预测函数，自动返回预测值，并包括残差，关联度，后验差检验。-GM (1,1) Grey forecasting function, automatic return predictive value, and includes the residuals, correlation, after test.
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Size: 1024 |
Author: Moonrise |
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Description: GM（1，1）源代码，计算原始数据的残差、关联度、小误差概率等。同时画出原始数据和预测数据对比图-GM (1,1) source code to calculate the raw data residuals, correlation, and so a small error probability. At the same time draw the raw data and forecast data comparison chart
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Size: 4096 |
Author: zoe |
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