时序预测 | MATLAB实现EMD-iCHOA+GRU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
预测效果
基本介绍
EMD-iCHOA+GRU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-iCHOA+GRU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。
clike
%% 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 < 2
error('ELM:Arguments','Not enough input arguments.');
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin < 3
N = size(P,2);
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin < 4
TF = 'sig';
end
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
if nargin < 5
TYPE = 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 == 1
T = 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 TF
case 'sig'
H = 1 ./ (1 + exp(-tempH));
case 'sin'
H = sin(tempH);
case 'hardlim'
H = hardlim(tempH);
end
% Calculate the Output Weight Matrix
LW = pinv(H') * T';
%--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------参考资料
[1] https://blog.csdn.net/article/details/126072792?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/article/details/126044265?spm=1001.2014.3001.5502