神经网络matlab简单程序

function [y1]=neural_k(p) k1=1; k2=2; k3=4; k4=8; n=5; t1=1+sin(k1*pi/4*p); t2=1+sin(k2*pi/4*p); t3=1+sin(k3*pi/4*p); t4=1+sin(k4*pi/4*p); net=newff(minmax(p),[n,1],{'tansig','purelin'},'trainlm'); net.trainParam.epochs=100; net.trainParam.goal=0.0001; net.trainParam.show=100; net=train(net,p,t1); net1=train(net,p,t2); net2=train(net,p,t3); net3=train(net,p,t4); figure; y1=sim(net,p); y2=sim(net1,p); y3=sim(net2,p); y4=sim(net3,p); subplot(2,2,1) plot(p,y1,':',p,t1,'-') legend('\fontsize{6}\bf训练后输出','\fontsize{6}\bf目标输出') xlabel('\fontsize{9}\bfp (k=1)') ylabel('\fontsize{9}\bf输出a/t') subplot(2,2,2) plot(p,y2,':',p,t2,'-') legend('\fontsize{6}\bf训练后输出','\fontsize{6}\bf目标输出') xlabel('\fontsize{9}\bfp (k=2)') ylabel('\fontsize{9}\bf输出a/t') subplot(2,2,3) plot(p,y3,':',p,t3,'-') legend('\fontsize{6}\bf训练后输出','\fontsize{6}\bf目标输出') xlabel('\fontsize{9}\bfp (k=4)') ylabel('\fontsize{9}\bf输出a/t') subplot(2,2,4) plot(p,y4,':',p,t4,'-') legend('\fontsize{6}\bf训练后输出','\fontsize{6}\bf目标输出') xlabel('\fontsize{9}\bfp (k=8)') ylabel('\fontsize{9}\bf输出a/t')