main_vq_pca.m

用Cross validation的方法建立人工神经网络的模型！

function main_VQ_PCA

clear
clc

i = 0;
for blocksize = 2: 2: 20
    nwds = 150;
    i = i + 1;
    [mse_VQ1(i),R2_VQ1(i),accu_VQ1(i)] = main_VQ(blocksize, nwds);
    [mse_PCA1(i),R2_PCA1(i),accu_PCA1(i)] = main_PCA(blocksize);
end


i = 0;
for nwds = 20: 10: 300
    blocksize = 16;
    i = i + 1;
    [mse_VQ(i),R2_VQ(i),accu_VQ(i)] = main_VQ(blocksize, nwds);
    [mse_PCA(i),R2_PCA(i),accu_PCA(i)] = main_PCA(blocksize);
end


mse_VQ1
R2_VQ1
accu_VQ1

mse_PCA1
R2_VQ1
accu_PCA1


mse_VQ
R2_VQ
accu_VQ

mse_PCA
R2_VQ
accu_PCA



figure;
plot(mse_VQ1);
hold on;
plot(mse_PCA1,'--');
hold off;

figure;
plot(R2_VQ1);
hold on;
plot(R2_PCA1,'--');
hold off;

figure;
plot(accu_VQ1);
hold on;
plot(accu_PCA1,'--');
hold off;



figure;
plot(mse_VQ);
hold on;
plot(mse_PCA,'--');
hold off;

figure;
plot(R2_VQ);
hold on;
plot(R2_PCA,'--');
hold off;

figure;
plot(accu_VQ);
hold on;
plot(accu_PCA,'--');
hold off;