zhenji.m

用神经网络预测震级

p=[0 0 0.62 0 0 0 0 ;
0.3195 0.4741 0.77 0.5 0.5 1 0.3158;
0.2835 0.5402 0.68 0 0.5 1 0.3158;
0.6210 1.0000 0.63 1 0.5 1 1.0000;
0.4185 0.4183 0.67 0.5 0 1 0.7368;
0.2160 0.4948 0.71 0 0 1 0.2632;
0.9990 0.0383 0.75 0.5 1 1 0.9474;
0.5805 0.4925 0.71 0 0 0 0.3684;
0.0810 0.0692 0.76 0 0 0 0.0526;
0.3915 0.1230 0.98 0.5 0 0 0.9874]';
t=[0 0.5313 0.5938 0.9375 0.4375 0.5000 1.0000 0.3750 0.3125 0.6563];
p_test=[0.0270 0.0742 0.62 0 0 0 0.2105;
    0.1755 0.3667 0.77 0 0.5 1 0.7368;
    0.4320 0.3790 0.68 0.5 0 1 0.2632;
    0.4995 0.4347 0.63 0 0 1 0.6842;
    0.6885 0.5842 0.67 0.5 0.5 1 0.4211;
    0.5400 0.8038 0.71 0.5 0.5 1 0.5789;
    0.1620 0.2565 0.75 0 0 1 0.4737]';
t_test=[0.1875 0.4062 0.4375 0.5983 0.6250 0.7187 0.3750];
threshold=[0 1;0 1;0 1;0 1;0 1;0 1;0 1];
a=[10 15 20];
for i=1:3
    net=newff(threshold, [a(i),1],{'tansig','logsig'},'traingdx');
    net.trainparam.epochs=100000;
    net.trainparam.goal=0.00001;
    net=init(net);
    net=train(net,p,t);
    y(i,:)=sim(net,p_test);
end 
figure;
plot(1:7,y(1,:)-t_test,'+');
hold on;
plot(1:7,y(2,:)-t_test,'--');
hold on;
plot(1:7,y(3,:)-t_test);
hold off;