我要评论一、神经网络基本概念
二、最初的神经网络模型——m-p模型
三、感知机模型
四、bp神经网络
% solve an input-output fitting problem with a neural network
% script generated by neural fitting app
% created 23-mar-2024 10:31:29
%
% this script assumes these variables are defined:
%
% iris_1 - input data.
% iris_2 - target data.
x = iris_1';
t = iris_2';
% choose a training function
% for a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. suitable in low memory situations.
trainfcn = 'trainlm'; % levenberg-marquardt backpropagation.
% create a fitting network
hiddenlayersize = 10;
net = fitnet(hiddenlayersize,trainfcn);
% setup division of data for training, validation, testing
net.divideparam.trainratio = 60/100;
net.divideparam.valratio = 20/100;
net.divideparam.testratio = 20/100;
% train the network
[net,tr] = train(net,x,t);
% test the network
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
% view the network
view(net)
% plots
% uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, ploterrhist(e)
%figure, plotregression(t,y)
%figure, plotfit(net,x,t)五、卷积神经网络
六、循环神经网络
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