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目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码实现
💥1 概述
支持向量机(SVM)以结构风险最小化为基本原则，可以实现风险的最小化控制，最小二乘支持向量机(LS-SVM)在继承SVM优点的基础上进行了相应的改进，通过平方项优化指标，以等式约束条件替换原来的不等式约束条件，可以加快求解速度。应用LS-SVM算法，可以有效处理非线性问题，可以选择应用其中的 RBF 核函数 K：
式中：x 为输入向量，xi 为第 i 个核函数的中心；σ 为核宽度，控制着核函数距中心点的宽度。
📚2 运行结果
编辑
编辑
部分代码：
X=(-10:0.1:10)';
Y = cos(X) + cos(2*X) + 0.1.*rand(length(X),1);
Xtrain = X(1:2:length(X));
Ytrain = Y(1:2:length(Y));
Xtest = X(2:2:length(X));
Ytest = Y(2:2:length(Y));
%%
sigs = [0.1 0.7 10 0.1 0.7 10 0.1 0.7 10]; gammas=[1 1 1 10 10 10 100 100 100];
for i=1:length(gammas)
gam = gammas(i);
sig2 = sigs(i);
mdl_in = {Xtrain,Ytrain,'f',gam,sig2,'RBF_kernel'};
[alpha,b] = trainlssvm(mdl_in);
subplot(3, 3, i);
plotlssvm(mdl_in, {alpha,b});
YtestEst = simlssvm(mdl_in, {alpha,b},Xtest);
plot(Xtest,Ytest,'.');
hold on;
plot(Xtest,YtestEst,'r+');
%legend('Ytest','YtestEst');
title(['sig2=' num2str(sig2) ',gam=' num2str(gam)]);
hold off
end
%%
cost_crossval = crossvalidate({Xtrain,Ytrain,'f',gam,sig2},10);
cost_loo = leaveoneout({Xtrain,Ytrain,'f',gam,sig2});
optFun = 'gridsearch';
globalOptFun = 'csa';
mdl_in = {Xtrain,Ytrain,'f',[],[],'RBF_kernel',globalOptFun};
[gam,sig2,cost] = tunelssvm(mdl_in, optFun,'crossvalidatelssvm',{10,'mse'})
% mdl_in = {Xtrain,Ytrain,'f',gam,sig2,'RBF_kernel'};
% [alpha,b] = trainlssvm(mdl_in);
% plotlssvm(mdl_in, {alpha,b});
% YtestEst = simlssvm(mdl_in, {alpha,b},Xtest);
% plot(Xtest,Ytest,'.');
% hold on;
% plot(Xtest,YtestEst,'r+');
% legend('Ytest','YtestEst');
%%
optFun = 'gridsearch';
globalOptFun = 'csa';
mdl_in = {Xtrain,Ytrain,'f',[],[],'RBF_kernel',globalOptFun};
tic
for i=1:20
[gam_csa_grid(i),sig2_csa_grid(i),cost_csa_grid(i)] = tunelssvm(mdl_in, optFun,'crossvalidatelssvm',{10,'mse'});
end
t1=toc;
t1=t1/20;
[c,idx]=min(cost_csa_grid); a=gam_csa_grid(idx);
fprintf('min=%0.5f \nmean=%0.5f \nvar=%0.5f \n', c, mean(cost_csa_grid), var(cost_csa_grid))
b=sig2_csa_grid(idx);
fprintf('t=%0.5f s \ngam=%0.5f \nsig2=%0.5f \n', mean(t1), a, b)
%%
optFun = 'simplex';
globalOptFun = 'csa';
mdl_in = {Xtrain,Ytrain,'f',[],[],'RBF_kernel',globalOptFun};
tic
for i=1:20
[gam_csa_simplex(i),sig2_csa_simplex(i),cost_csa_simplex(i)] = tunelssvm(mdl_in, optFun,'crossvalidatelssvm',{10,'mse'});
end
t1=toc;
t1=t1/20;
[c,idx]=min(cost_csa_simplex); a=gam_csa_simplex(idx); b=sig2_csa_simplex(idx);
fprintf('min=%0.5f \nmean=%0.5f \nvar=%0.5f \n', c, mean(cost_csa_simplex), var(cost_csa_simplex))
fprintf('t=%0.5f s \ngam=%0.5f \nsig2=%0.5f \n', mean(t1), a, b)
%%
optFun = 'gridsearch';
globalOptFun = 'ds';
mdl_in = {Xtrain,Ytrain,'f',[],[],'RBF_kernel',globalOptFun};
tic
for i=1:20
[gam_ds_grid(i),sig2_ds_grid(i),cost_ds_grid(i)] = tunelssvm(mdl_in, optFun,'crossvalidatelssvm',{10,'mse'});
end
t1=toc;
t1=t1/20;
[c,idx]=min(cost_ds_grid); a=gam_ds_grid(idx); b=sig2_ds_grid(idx);
fprintf('min=%0.5f \nmean=%0.5f \nvar=%0.5f \n', c, mean(cost_ds_grid), var(cost_ds_grid))
fprintf('t=%0.5f s \ngam=%0.5f \nsig2=%0.5f \n', mean(t1), a, b)
%%
optFun = 'simplex';
globalOptFun = 'ds';
mdl_in = {Xtrain,Ytrain,'f',[],[],'RBF_kernel',globalOptFun};
tic
for i=1:20
[gam_ds_simplex(i),sig2_ds_simplex(i),cost_ds_simplex(i)] = tunelssvm(mdl_in, optFun,'crossvalidatelssvm',{10,'mse'});
end
t1=toc;
t1=t1/20;
[c,idx]=min(cost_ds_simplex); a=gam_ds_simplex(idx); b=sig2_ds_simplex(idx);
fprintf('min=%0.5f \nmean=%0.5f \nvar=%0.5f \n', c, mean(cost_ds_simplex), var(cost_ds_simplex))
fprintf('t=%0.5f s \ngam=%0.5f \nsig2=%0.5f \n', mean(t1), a, b)
%%
sig2 = 0.5; gam = 10;
criterion_L1 = bay_lssvm({Xtrain,Ytrain,'f',gam,sig2},1)
criterion_L2 = bay_lssvm({Xtrain,Ytrain,'f',gam,sig2},2)
criterion_L3 = bay_lssvm({Xtrain,Ytrain,'f',gam,sig2},3)
%%
gam=100; sig2=0.05;
[~,alpha,b] = bay_optimize({Xtrain,Ytrain,'f',gam,sig2}, 1);
[~,gam] = bay_optimize({Xtrain,Ytrain,'f',gam,sig2},2);
[~,sig2] = bay_optimize({Xtrain,Ytrain,'f',gam,sig2},3);
sig2e = bay_errorbar({Xtrain,Ytrain,'f',gam,sig2},'figure');
%%
load iris;
gam=5; sig2=0.75;
cnt=1;
for gam=[1 10 100]
for sig2=[0.2 1 10]
subplot(3,3,cnt);
bay_modoutClass({X,Y,'c',gam,sig2},'figure');
cnt=cnt+1;
end
end
%%
X = 10.*rand(100,3)-3;
Y = cos(X(:,1)) + cos(2*(X(:,1))) +0.3.*randn(100,1);
[selected, ranking, costs2] = bay_lssvmARD({X,Y,'class', 100, 0.1});
%%
X = (-10:0.2:10)';
Y = cos(X) + cos(2*X) +0.1.*rand(size(X));
out = [15 17 19];
Y(out) = 0.7+0.3*rand(size(out));
out = [41 44 46];
Y(out) = 1.5+0.2*rand(size(out));
mdl_in = {X, Y,'f', 100, 0.1,'RBF_kernel'};
[alpha,b] = trainlssvm(mdl_in);
plotlssvm(mdl_in, {alpha,b});
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1]赵舵.基于天气类型聚类和LS-SVM的光伏出力预测方法[J].光源与照明,2022(6):82-84
[2]教传艳.基于自适应LS-SVM的柴油机废气再循环冷却控制系统设计[J].计算机测量与控制,2022,30(2):124-128144