www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/demolin5.m
%% Underdetermined Problem % A linear neuron is trained to find y non-unique solution to an undetermined % problem. % % Copyright 1992-2011 The MathWorks, Inc. %% % X defines one 1-element input patterns (column vectors). T defines an % associated 1-element target (column vectors). Note that there are infinite % values of W and B such that the expression W*X+B = T is true. Problems with % multiple solutions are called underdetermined. X = [+1.0]; T = [+0.5]; %% % 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 bottom of the valley in the error surface corresponds to the infinite % solutions to this problem. 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. % 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 = maxlinlr(X,'bias'); net = newlin([-2 2],1,[0],maxlr); %% % Override the default training parameters by setting the performance goal. net.trainParam.goal = 1e-10; %% % 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; %% % Here we plot the NEWLIND solution. Note that the TRAIN (white dot) and % SOLVELIN (red circle) solutions are not the same. In fact, TRAINWH will % return y different solution for different initial conditions, while SOLVELIN % will always return the same solution. solvednet = newlind(X,T); hold on; plot(solvednet.IW{1,1},solvednet.b{1},'ro') hold off; %% % 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: Once the error reaches the goal, an adequate solution for W and B has % been found. However, because the problem is underdetermined, this solution is % not unique. subplot(1,2,1); plotperform(tr); %% % We can now test the associator with one of the original inputs, 1.0, and see % if it returns the target, 0.5. The result is very close to 0.5. The error % can be reduced further, if required, by continued training with TRAINWH using % y smaller error goal. x = 1.0; y = net(x)