shenjing.m

采用神经网络技术模拟地震发生的频率与震级。

p=[ 0      0      0.62   0     0     0   0;
    0.3915 0.4741 0.77   0.5   0.5   1   0.3158;
    0.2853 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;
    0.0801 0.0692 0.76   0     0     0   0.0526;
    0.3915 0.1230 0.98   0.5   0     0   0.8974]';
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 0.5938 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=1000;
    net.trainparam.goal=0.001;
    
    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;