用newff模拟sin函数

用newff模拟sin函数

% http://blog.csdn.net/superdont 我思故我在 P=-1:0.1:1; %建立目标值，是sin曲线上均匀取到的21个点 T=0:0.314:6.28 T=sin(T); %创建网络 net=newff(minmax(P),[5,1],{'tansig','purelin'},'traingda'); %newff :Create a feed-forward backpropagation network %traingda: TRAINGDA Gradient descent with adaptive lr backpropagation. net.trainParam.show = 50; %系统每50步显示一次训练误差的变化曲线 net.trainParam.lr = 0.05; %学习速率 net.trainParam.lr_inc = 1.08; %Ratio to increase learning rate net.trainParam.lr_dec = 0.6; %Ratio to decrease learning rate net.trainParam.epochs = 2000; %训练步数 net.trainParam.goal = 9.5238e-004; % sse=0.02 %训练 网络 [net,tr]=train(net,P,T); % train trains a network net according to net.trainFcn and net.trainParam. % train(NET,P,T,Pi,Ai,VV,TV) takes, % net -- Neural Network 函数返回值，训练后的神经网络 % P -- Network inputs % T -- Network targets, default = zeros % Pi -- Initial input delay conditions, default = zeros % Ai -- Initial layer delay conditions, default = zeros % VV -- Structure of validation vectors, default = [] % TV -- Structure of test vectors, default = [] % and returns, % net -- New network % TR -- Training record (epoch and perf) 函数返回值，训练记录，步数和性能 % Y -- Network outputs % E -- Network errors. % Pf -- Final input delay conditions % Af -- Final layer delay conditions figure(1) plot(tr.lr); figure(2) plot(tr.perf); %%显示预测结果 %应用生成的网络对P进行模拟 T1=sim(net,P); figure(3); %显示目标值 plot(P,T,'r*'); hold on; %显示模拟值 plot(P,T1,'g*');