我找不到与 对应的内置神经网络工具箱BpTrainingProcess()，因此这必须是您可以在本地访问的文件（或者您需要从给您此代码的人那里获取）。它可能将几个对神经网络工具箱函数的函数调用串在一起，或者可能是反向传播训练方法的原始实现。
否则，代码有一些缺点。一方面，内部 if-else 语句似乎并没有做任何事情。即使是被注释掉的行也会留下一个完全无用的 if-else 设置。看起来 if-else 旨在让您对从 Excel 文件的 Sheet1 加载的数据与从 Sheet2 加载的数据进行不同的标签规范化。也许这对您很重要，但目前还没有在程序中发生。
最后，代码使用一个空数组，label然后将行追加到空数组。这是不需要的，因为您已经知道将有多少行（总计size(N,1)*2 = 150*2 = 300行。您可以轻松设置label=zeros(300,1)然后在 for 循环的每次迭代中使用通常的索引：label(i) = ...。这可以节省时间和空间，但可以说对于 300 行的数据集来说并不重要（假设每行的长度不太大）。
我将文档放在下面的代码旁边。
% The functionn 'xlsread()' reads data from an Excel file.
% Here it is storing the values from Sheet 1 of the file 'data.xls'
% into the variable N, and then using the syntax N = N(1:150,:) to
% change N from being all of the data into being only the first
% 150 rows of the data
N = xlsread('data.xls','Sheet1');
N = N(1:150,:);
% Now do the same thing for Sheet 2 from the Excel file.
UN = xlsread('data.xls','Sheet2');
UN = UN(1:150,:);
% This concatenates the two different data arrays together, making
% one large array where N is the top half and UN is the bottom half.
% This is basically just stacking N on top of UN into one array.
traindata = [N ; UN];
% This saves a copy of the newly stacked array into the Matlab data file
% 'traindata.mat'. From now on, you should be able to load the data from
% this file, without needing to read it from the Excel sheet above.
save('traindata.mat','traindata');
% This makes an empty array which will have new things appended to it below.
label = [];
% Because UN and N have the same number of rows, then the training data
% has twice as many rows. So this sets up a for loop that will traverse
% all of these rows of the training data. The 'size()' function can be
% used to get the different dimensions of an array.
for i = 1 : size(N,1)*2
% Here, an if statement is used to check if the current row number, i,
% is less than or equal to than the number of rows in N. This implies
% that this part of the if-statement is only for handling the top half
% of 'trainingdata', that is, the stuff coming from the variable N.
if( i <= size(N,1))
% The line below was already commented out. Maybe it had an old use
% but is no longer needed?
% label = [label ;sum(traindata(i,:))/size(traindata(i,:),2)];
% This syntax will append new rows to the variable 'label', which
% started out as an empty array. This is usually bad practice, memory-wise
% and also for readability.
% Here, the sum of the training data is being computed, and divided by 10
% in every case, and then appended as a new row in 'label'. Hopefully,
% if you are familiar with the data, you will know why the data in 'N'
% always needs to be divided by 10.
label = [label ;sum(traindata(i,:))/10];
% Otherwise, if i > # of rows then handle the data differently.
% Really this means the code below treats only data from the variable UN.
else
% The line below was already commented out. Maybe it had an old use
% but is no longer needed?
% label = [label ;sum(traindata(i,:))/size(traindata(i,:),2)];
% Just like above, the data is being divided by 10. Given that there
% is nothing different about the code here, and how it modifies 'label'
% there is no need for the if-else statements, and they only waste time.
label = [label ;sum(traindata(i,:))/10];
% This is needed to show the end of the if-else block.
end
% This is needed to show the end of the for-loop.
end
% This appears to be a Back-Propagation Neural Network training function.
% This doesn't match any built-in Matlab function I can find, but you might
% check in the Neural Network toolbox to see if the local function
% BpTrainingProcess is a wrapper for a collection of built-in training functions.
weightMat = BpTrainingProcess(4, 0.0001, 0.1, 0.9, 15,
[size(traindata,1) 1], traindata,label);
这是用于反向传播训练的示例 Matlab 神经网络工具箱函数的链接。您可能想查看那里的文档，看看是否有任何类似于BpTrainingProcess().