www.gusucode.com > nnet 案例源码 matlab代码程序 > nnet/RefFitNetExample.m
%% Construct and Train a Function Fitting Network % Load the training data. [x,t] = simplefit_dataset; %% % The 1-by-94 matrix |x| contains the input values and the 1-by-94 matrix |t| % contains the associated target output values. %% % Construct a function fitting neural network with one hidden layer of size % 10. net = fitnet(10); %% % View the network. view(net) %% % The sizes of the input and output are zero. The software adjusts % the sizes of these during training according to the training data. %% % Train the network |net| using the training data. net = train(net,x,t); %% % View the trained network. view(net) %% % You can see that the sizes of the input and output are 1. %% % Estimate the targets using the trained network. y = net(x); %% % Assess the performance of the trained network. The default performance function is mean squared error. perf = perform(net,y,t) %% % The default training algorithm for a function fitting network is % Levenberg-Marquardt ( |'trainlm'| ). Use the Bayesian regularization training algorithm and % compare the performance results. net = fitnet(10,'trainbr'); net = train(net,x,t); y = net(x); perf = perform(net,y,t) %% % The Bayesian regularization training algorithm improves the performance of the network in terms of % estimating the target values.