www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/demolin1.m
%% Pattern Association Showing Error Surface % A linear neuron is designed to respond to specific inputs with target outputs. % % Copyright 1992-2011 The MathWorks, Inc. %% % X defines two 1-element input patterns (column vectors). T defines the % associated 1-element targets (column vectors). 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 = -1:0.1:1; b_range = -1:0.1:1; ES = errsurf(X,T,w_range,b_range,'purelin'); plotes(w_range,b_range,ES); %% % The function NEWLIND will design y network that performs with the minimum % error. net = newlind(X,T); %% % SIM is used to simulate the network for inputs X. We can then calculate the % neurons errors. SUMSQR adds up the squared errors. A = net(X) E = T - A SSE = sumsqr(E) %% % PLOTES replots the error surface. PLOTEP plots the "position" of the network % using the weight and bias values returned by SOLVELIN. As can be seen from % the plot, SOLVELIN found the minimum error solution. plotes(w_range,b_range,ES); plotep(net.IW{1,1},net.b{1},SSE); %% % We can now test the associator with one of the original inputs, -1.2, and see % if it returns the target, 1.0. x = -1.2; y = net(x)