MATLAB实现VMD-SSA-KELM和VMD-KELM变分模态分解结合麻雀算法优化核极限学习机时间序列预测（完整源码和数据）

%_________________________________________________________________________% % 基于VMD和麻雀算法优化的核极限学习机(VMD-SSA-KELM)和VMD核极限学习机(VMD-KELM)时间序列预测 %_________________________________________________________________________% clear close all clc %% 导入数据 WTData = xlsread('WTData.xlsx', 'Sheet1', 'B2:B3673'); vmd(WTData,'NumIMFs',3) [imf,res] = vmd(WTData,'NumIMFs',3); Data=[imf res]; for k = 1 : size(Data, 2) disp(['第 ',num2str(k),' 个分量的预测: ']) data(:,k) = Data(:, k); delay=1; %时滞 data1 = []; for i=1:length(data(:,k))-delay data1(i,:)=data(i:i+delay,k)'; end P = data1(:,1:end-1); T = data1(:,end); % 设定训练集与测试集 Len = size(data1, 1); % testNum = 300; testNum = round(Len*0.3); trainNum = Len - testNum; % 训练集 P_train = P(1:trainNum, :)'; T_train = T(1:trainNum, :)'; % 测试集 P_test = P(trainNum+1:trainNum+testNum,:)'; T_test = T(trainNum+1:trainNum+testNum,:)'; %% 归一化 [Pn_train ,ps]= mapminmax(P_train, 0, 1); [Tn_train, ts] = mapminmax(T_train, 0, 1); %测试集的归一化 Pn_test = mapminmax('apply', P_test, ps); Tn_test = mapminmax('apply', T_test, ts); %% 麻雀参数设置 pop=20; %种群数量 Max_iteration=30; % 设定最大迭代次数 dim = 2;% 维度为2，即优化两个参数，正则化系数 C 和核函数参数 S lb = [0.0001,0.0001];%下边界 ub = [50,50];%上边界 fobj = @(x) fun(x,Pn_train,Tn_train,Pn_test,T_test,ts); [Best_pos,Best_score,SSA_curve]=SSA(pop,Max_iteration,lb,ub,dim,fobj); %开始优化 figure plot(SSA_curve,'linewidth',1.5); grid on; xlabel('迭代次数') ylabel('适应度值') title('麻雀收敛曲线') %% 获取最优正则化系数 C 和核函数参数 S Regularization_coefficient = Best_pos(1); Kernel_para = Best_pos(2); Kernel_type = 'rbf'; %% 训练 [~,OutputWeight] = kelmTrain(Pn_train,Tn_train,Regularization_coefficient,Kernel_type,Kernel_para); % 测试 InputWeight = OutputWeight; [TestOutT] = kelmPredict(Pn_train,InputWeight,Kernel_type,Kernel_para,Pn_test); TestOut(k,:) = mapminmax('reverse',TestOutT,ts); %% 基础KELM进行预测 Regularization_coefficient1 = 10; Kernel_para1 = [4]; %核函数参数矩阵 Kernel_type = 'rbf'; %% 训练 [~,OutputWeight1] = kelmTrain(Pn_train,Tn_train,Regularization_coefficient1,Kernel_type,Kernel_para1); %% 预测 InputWeight1 = OutputWeight1; [TestOutT1] = kelmPredict(Pn_train,InputWeight1,Kernel_type,Kernel_para1,Pn_test); TestOut1(k,:) = mapminmax('reverse',TestOutT1,ts); end TestOutT = sum(TestOut); TestOutT1 = sum(TestOut1); T = sum(data'); T_test = T(end - testNum + 1 : end); disp('VMD-KELM误差指标计算: ') [e1,ape1]=caculate_perf(T_test, TestOutT1); disp(' ') disp('VMD-SSA-KELM误差指标计算: ') [e,ape]=caculate_perf(T_test, TestOutT); figure plot(T_test, 'b-','LineWidth',1.0) hold on plot(TestOutT1, 'k-','LineWidth',1.0) hold on plot(TestOutT, 'r-','LineWidth',1.0) xlabel('测试样本') ylabel('值') legend('实际值', 'VMD-KELM', 'VMD-SSA-KELM') grid on title('基于VMD的SSA优化KELM模型测试集的预测值和实际值对比图') figure plot(e1, 'k-','LineWidth',1.0) hold on plot(e, 'r-','LineWidth',1.0) xlabel('测试样本') ylabel('误差') legend('VMD-KELM', 'VMD-SSA-KELM') grid on title('基于VMD的SSA优化KELM模型测试集的预测误差对比图')