响应式网站和自适应网站的区别,网站开发要用什么语言,网站建设的参考文献英文,网页制作与网站建设时序预测 | MATLAB实现EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测 目录 时序预测 | MATLAB实现EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测预测效果基本介绍程序设计参考资料 预测效果 基本介绍 EMD-iCHOAGR…时序预测 | MATLAB实现EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测 目录 时序预测 | MATLAB实现EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测预测效果基本介绍程序设计参考资料 预测效果 基本介绍 EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测 1.时间序列单列输入如需多特征输入需额外付费。经过EMD分解后利用优化后的GRU对每个分量进行预测最后集成相加算法新颖EMD也可以换成其他分解方法GRU也可以换成BiLSTM等其他预测模型。 2.iCHOA改进的黑猩猩优化算法改进点如下 [1]利用Sobol序列初始化种群增加种群的随机性和多样性为算法全局寻优奠定基础 [2]其次引入基于凸透镜成像的反向学习策略将其应用到当前最优个体上产生新的个体提高算法的收敛精度和速度 [3]最后将水波动态自适应因子添加到攻击者位置更新处增强算法跳出局部最优的能力。 3.直接替换Excel数据即可用注释清晰适合新手小白 4.附赠测试数据输入格式如图3所示可直接运行 程序设计
完整程序和数据下载方式私信博主回复MATLAB实现EMD-iCHOAGRU基于经验模态分解-改进黑猩猩算法优化门控循环单元的时间序列预测。
%% 参数设置
%% 训练模型
%% 模型预测%% 数据反归一化
T_sim1 mapminmax(reverse, t_sim1, ps_output);
T_sim2 mapminmax(reverse, t_sim2, ps_output);
function [IW,B,LW,TF,TYPE] elmtrain(P,T,N,TF,TYPE)
% ELMTRAIN Create and Train a Extreme Learning Machine
% Syntax
% [IW,B,LW,TF,TYPE] elmtrain(P,T,N,TF,TYPE)
% Description
% Input
% P - Input Matrix of Training Set (R*Q)
% T - Output Matrix of Training Set (S*Q)
% N - Number of Hidden Neurons (default Q)
% TF - Transfer Function:
% sig for Sigmoidal function (default)
% sin for Sine function
% hardlim for Hardlim function
% TYPE - Regression (0,default) or Classification (1)
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
% Output
% IW - Input Weight Matrix (N*R)
% B - Bias Matrix (N*1)
% LW - Layer Weight Matrix (N*S)
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
% Example
% Regression:
% [IW,B,LW,TF,TYPE] elmtrain(P,T,20,sig,0)
% Y elmtrain(P,IW,B,LW,TF,TYPE)
% Classification
% [IW,B,LW,TF,TYPE] elmtrain(P,T,20,sig,1)
% Y elmtrain(P,IW,B,LW,TF,TYPE)
% See also ELMPREDICT
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin 2error(ELM:Arguments,Not enough input arguments.);
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin 3N size(P,2);
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin 4TF sig;
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin 5TYPE 0;
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if size(P,2) ~ size(T,2)error(ELM:Arguments,The columns of P and T must be same.);
end
[R,Q] size(P);
if TYPE 1T ind2vec(T);
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
[S,Q] size(T);
% Randomly Generate the Input Weight Matrix
IW rand(N,R) * 2 - 1;
% Randomly Generate the Bias Matrix
B rand(N,1);
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
BiasMatrix repmat(B,1,Q);
% Calculate the Layer Output Matrix H
tempH IW * P BiasMatrix;
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
switch TFcase sigH 1 ./ (1 exp(-tempH));case sinH sin(tempH);case hardlimH hardlim(tempH);
end
% Calculate the Output Weight Matrix
LW pinv(H) * T;
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
参考资料 [1] https://blog.csdn.net/article/details/126072792?spm1001.2014.3001.5502 [2] https://blog.csdn.net/article/details/126044265?spm1001.2014.3001.5502
