trainlm.m

bp神经网路在地震预测中的应用

P=[0.3762 0.6084 0.4778 0.9000 0.7166 0.2992;
0.8841 0.9000 0.2851 0.6000 0.9000 0.5489; 
0.7889 0.6084 0.2973 0.7000 0.6767 0.5152;
0.5508 0.3325 0.9000 0.8250 0.6821 0.5726; 
0.9000 0.2261 0.8373 0.7500 0.4721 0.9000;
0.7730 0.2655 0.5237 0.1000 0.2116 0.4679;
0.3841 0.2143 0.5268 0.1500 0.2063 0.2519; 
0.4397 0.2143 0.1000 0.2750 0.2887 0.3295;
0.1000 0.1000 0.2178 0.1000 0.1000 0.1000; 
0.2730 0.5571 0.2698 0.6250 0.7060 0.2215;
0.8048 0.7818 0.3937 0.7250 0.8017 0.5523;
0.2619 0.2182 0.2315 0.4250 0.2914 0.2384]';
T=[0.5267; 0.5267; 0.9000; 0.3667; 0.8467; 0.3667; 0.3667; 0.6867; 0.1000; 0.6867; 0.6333; 0.4733]';
threshold=[0.1 0.9; 0.1 0.9; 0.1 0.9; 0.1 0.9; 0.1 0.9; 0.1 0.9];
net=newff(threshold,[8 1],{'tansig' 'logsig'},'trainlm');
net.trainParam.show=50;
net.trainParam.epochs=2000;
net.trainParam.goal=0.001;
net=train(net,P,T)
y=sim(net,P);
error=y-T ;
res=norm(error)