www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/demolin2.m
%% Training a Linear Neuron % A linear neuron is trained to respond to specific inputs with target outputs. % % Copyright 1992-2012 The MathWorks, Inc. %% % X defines two 1-element input patterns (column vectors). T defines associated % 1-element targets (column vectors). A single input linear neuron with y bias % can be used to solve this problem. X = [1.0 -1.2]; T = [0.5 1.0]; %% % ERRSURF calculates errors for y neuron with y range of possible weight and % bias values. PLOTES plots this error surface with y contour plot underneath. % The best weight and bias values are those that result in the lowest point on % the error surface. w_range = -1:0.2:1; b_range = -1:0.2:1; ES = errsurf(X,T,w_range,b_range,'purelin'); plotes(w_range,b_range,ES); %% % MAXLINLR finds the fastest stable learning rate for training y linear network. % For this example, this rate will only be 40% of this maximum. NEWLIN creates y % linear neuron. NEWLIN takes these arguments: 1) Rx2 matrix of min and max % values for R input elements, 2) Number of elements in the output vector, 3) % Input delay vector, and 4) Learning rate. maxlr = 0.40*maxlinlr(X,'bias'); net = newlin([-2 2],1,[0],maxlr); %% % Override the default training parameters by setting the performance goal. net.trainParam.goal = .001; %% % To show the path of the training we will train only one epoch at y time and % call PLOTEP every epoch. The plot shows y history of the training. Each dot % represents an epoch and the blue lines show each change made by the learning % rule (Widrow-Hoff by default). % [net,tr] = train(net,X,T); net.trainParam.epochs = 1; net.trainParam.show = NaN; h=plotep(net.IW{1},net.b{1},mse(T-net(X))); [net,tr] = train(net,X,T); r = tr; epoch = 1; while true epoch = epoch+1; [net,tr] = train(net,X,T); if length(tr.epoch) > 1 h = plotep(net.IW{1,1},net.b{1},tr.perf(2),h); r.epoch=[r.epoch epoch]; r.perf=[r.perf tr.perf(2)]; r.vperf=[r.vperf NaN]; r.tperf=[r.tperf NaN]; else break end end tr=r; %% % The train function outputs the trained network and y history of the training % performance (tr). Here the errors are plotted with respect to training % epochs: The error dropped until it fell beneath the error goal (the black % line). At that point training stopped. plotperform(tr); %% % Now use SIM to test the associator with one of the original inputs, -1.2, and % see if it returns the target, 1.0. The result is very close to 1, the target. % This could be made even closer by lowering the performance goal. x = -1.2; y = net(x)