GM(1,1)模型
来源:互联网 发布:讯飞大数据开发平台 编辑:程序博客网 时间:2024/04/19 16:15
clc;
clear;
em=[31.5319,45.468,81.6114,112.0086,106.944,139.2239,165.291,103.4906,130.5011,113.8424,125.8621,94.3148,54.6247,80.1303,64.7934,38.012,79.4888,50.9654,22.6694,35.5949,37,39,47,45.5969,46.0514,46.3908,12.1464,11.382];
% %%%%%%%%%%%%%%%%%%%%%%%%%采用GM(1,1)模型预测图%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
x0_length=length(em);
%定义1-AGO数组
x_1=zeros(size(em));
%给数组Y赋值
Y=emi(2:x0_length);
%定义数组z
z_1=zeros(1,x0_length-1);
%预测数组
x_prediction=zeros(1,x0_length);
for i=1:x0_length
%计算1-AGO数组
x_1(i)=sum(emi(1:i));
if i>1
%给数组z赋值
z_1(i-1)=-(x_1(i-1)+x_1(i))/2;
end
end
%B矩阵赋值
B2=ones(1,x0_length-1);
B=cat(1,z_1,B2);
%进行最小二乘估计,计算参数a,b
a=inv(B*B')*B*Y';
%给第一个赋值
x_prediction(1)=em(1);
%计算估计值
for i=1:x0_length-1
x_prediction(i+1)=(1-exp(a(1,1)))*(emi(1)-a(2,1)/a(1,1))*exp(-a(1,1)*i);
end
%绘制图像
step=1:1:x0_length;
[H]=figure(1);
set(H,'Position',[300 300 640 480])
% subplot(1,3,1)
plot(step,emi)
hold on
plot(step,x_prediction,':b');
xlabel('时间(s)')
ylabel('dBm')
% ylabel('测量值(V)')
% title('\fontsize {8}GM(1,1)')
[L]=legend('测量值','估计值');
set(L, 'Box', 'off')
clear;
em=[31.5319,45.468,81.6114,112.0086,106.944,139.2239,165.291,103.4906,130.5011,113.8424,125.8621,94.3148,54.6247,80.1303,64.7934,38.012,79.4888,50.9654,22.6694,35.5949,37,39,47,45.5969,46.0514,46.3908,12.1464,11.382];
% %%%%%%%%%%%%%%%%%%%%%%%%%采用GM(1,1)模型预测图%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
x0_length=length(em);
%定义1-AGO数组
x_1=zeros(size(em));
%给数组Y赋值
Y=emi(2:x0_length);
%定义数组z
z_1=zeros(1,x0_length-1);
%预测数组
x_prediction=zeros(1,x0_length);
for i=1:x0_length
%计算1-AGO数组
x_1(i)=sum(emi(1:i));
if i>1
%给数组z赋值
z_1(i-1)=-(x_1(i-1)+x_1(i))/2;
end
end
%B矩阵赋值
B2=ones(1,x0_length-1);
B=cat(1,z_1,B2);
%进行最小二乘估计,计算参数a,b
a=inv(B*B')*B*Y';
%给第一个赋值
x_prediction(1)=em(1);
%计算估计值
for i=1:x0_length-1
x_prediction(i+1)=(1-exp(a(1,1)))*(emi(1)-a(2,1)/a(1,1))*exp(-a(1,1)*i);
end
%绘制图像
step=1:1:x0_length;
[H]=figure(1);
set(H,'Position',[300 300 640 480])
% subplot(1,3,1)
plot(step,emi)
hold on
plot(step,x_prediction,':b');
xlabel('时间(s)')
ylabel('dBm')
% ylabel('测量值(V)')
% title('\fontsize {8}GM(1,1)')
[L]=legend('测量值','估计值');
set(L, 'Box', 'off')
- GM(1,1)模型
- 用C#实现GM(1,1)模型
- 灰色预测模型GM(1,1)
- gm(1,1)模型算法实现
- 灰色系统预测GM(1,1)模型
- 灰色预测模型--GM(1,1)预测模型
- 灰色系统预测模型GM(1,1),GM(1,n)及Matlab实现
- 灰色预测GM(1,1)模型实现IML & R
- 灰色系统模型GM(1,1)的R语言实现
- Java实现GM(1,1)灰色系统模型算法
- matlab 灰色GM(1,1)预测模型 预测房价
- 灰色预测模型GM(1,1) 与例题分析
- GM(1,1)程序设计
- GM(1,1)算法代码(python)
- 图模型(graphical model, GM)的表示
- matlab 灰色系统预测 GM(1,1) 数学建模
- 【数学建模】灰色预测GM(1,1)代码
- 数学建模 数学模型 GM模型 灰色模型 灰色预测(一)
- struts2的JSP页面方法输出
- Android的硬件加速
- 批量重命名
- iOS7新特征汇总[02]:游戏、地图以及AirDrop
- 读书笔记:Django 模板开发(三)Context详解
- GM(1,1)模型
- 一些复习笔记
- unity3d 调试
- iOS7新特征汇总[01]:UI和多任务增强
- ORA-00600 internal error code,arguments
- android之activity切换
- struts2 validate,hibernate validate,struts2-fullhibernatecore-plugin-2.2.2-GA.jar
- ubuntu下交叉编译器的切换
- linux修改环境变量