1 内容介绍
针对传统网络流量预测精度低难题,为了获得理想的网络流量预测结果,提出一种基于高斯过程回归(GPR)的网络流量预测模型.该模型首先计算延迟时间和嵌入维数,构建高斯过程回归的学习样本;然后采用高斯过程回归对网络流训练集进行学习,并采用入侵杂草优化对高斯过程回归的参数进行优化;最后采用经典的网络流量测试集对该模型性能进行实验测试.实验结果表明,高斯过程回归模型提高了网络流量的预测精度.
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2 仿真代码
%%%%%%%%%% Gaussian Process Regression (GPR) %%%%%%%%%
% Demo: prediction using GPR
% ---------------------------------------------------------------------%
clc
close all
clear all
addpath(genpath(pwd))
% load data
%{
x : training inputs
y : training targets
xt: testing inputs
yt: testing targets
%}
% multiple input-multiple output
load('./data/data_2.mat')
% Set the mean function, covariance function and likelihood function
% Take meanConst, covRQiso and likGauss as examples
meanfunc = @meanConst;
covfunc = @covRQiso;
likfunc = @likGauss;
% Initialization of hyperparameters
hyp = struct('mean', 3, 'cov', [2 2 2], 'lik', -1);
% meanfunc = [];
% covfunc = @covSEiso;
% likfunc = @likGauss;
%
% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);
% Optimization of hyperparameters
hyp2 = minimize(hyp, @gp, -5, @infGaussLik, meanfunc, covfunc, likfunc,x, y);
% Regression using GPR
% yfit is the predicted mean, and ys is the predicted variance
[yfit ys] = gp(hyp2, @infGaussLik, meanfunc, covfunc, likfunc,x, y, xt);
% Visualization of prediction results
% First output
plotResult(yt(:,1), yfit(:,1))
% Second output
plotResult(yt(:,2), yfit(:,2))
3 运行结果
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4 参考文献
[1]李振刚. 基于高斯过程回归的网络流量预测模型[J]. 计算机应用, 2014.
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部分理论引用网络文献，若有侵权联系博主删除。
