res = xlsread('Copy_of_数据集.xlsx'); input = res((1:120), 2:6)'; % 载入输入数据 output = res((1:120), 7:9)'; % 载入输出数据 % 划分训练集和测试集 input_train = input(:, 1:80); output_train = output(:, 1:80); input_test = input(:, 81:100); output_test = output(:, 81:100); % 归一化 [input_train_n, input_ps] = mapminmax(input_train, -1, 1); [output_train_n, output_ps] = mapminmax(output_train, -1, 1); % 建立模型 input_num = size(input_train_n, 1); % 输入层节点数量 hidden_num = 10; % 隐含层节点数量 output_num = size(output_train_n, 1); % 输出层节点数量 net = newff(input_train_n, output_train_n, hidden_num, {'tansig','purelin'}, 'trainlm'); net.trainParam.epochs = 15000; net.trainParam.lr = 0.01; net.trainParam.goal = 0.0001; % 训练模型 [net, tr] = train(net, input_train_n, output_train_n); % 测试模型 input_test_n = mapminmax('apply', input_test, input_ps); output_test_n = mapminmax('apply', output_test, output_ps); output_pred_n = sim(net, input_test_n); %%反归一化 output_test_pred = mapminmax('reverse', output_pred_n, output_ps); output_test_pred = round(output_test_pred); % 四舍五入取整 %%绘制预测结果和真实结果的对比图 figure; plot(output_test(1,:), 'bo-'); hold on; plot(output_test_pred(1,:), 'r*-'); legend('真实结果', '预测结果'); xlabel('样本编号'); ylabel('输出值'); title('预测结果和真实结果');这段程序当中如果进行误差补偿的话怎么操作
时间: 2023-12-22 12:04:33 浏览: 73
在这段程序中进行误差补偿,可以修改训练模型时的代码。假设误差补偿的数据已经存在一个名为“error”的变量中,则可以在训练模型前添加下面一行代码:
```
net.inputs{1}.processFcns = {'removeconstantrows', 'mapminmax', 'fixunknown', 'removeconstantcols', 'processpca', error};
```
其中,net.inputs{1}.processFcns表示输入数据的处理函数,需要将error添加到处理函数中。这样就可以对输入数据进行误差补偿了。在进行归一化时也需要注意,需要将归一化后的输入数据和误差补偿数据一起传递给mapminmax函数,代码如下:
```
[input_train_n, input_ps] = mapminmax(input_train, -1, 1, error);
```
同样的,在测试模型时也需要对输入数据进行误差补偿和归一化操作,代码如下:
```
input_test_n = mapminmax('apply', input_test, input_ps, error);
```
需要注意的是,误差补偿数据需要根据具体应用场景而定,需要根据实际情况进行选择和计算。同时,误差补偿也可能会影响模型的训练效果,需要进行实验测试来确定最优的误差补偿方式。
相关问题
代码解读,xlswrite('A_science_city_complexity.xlsx',city_complexity,'Sheet1','F2'); [num,txt,AAA]=xlsread('rca_2000.xlsx','Sheet1','A1:E306'); xlswrite('A_science_city_complexity.xlsx',AAA,'Sheet1','A1'); [num1,txt1]=xlsread('biaotou_city.xlsx','Sheet1','F1:T1'); xlswrite('A_science_city_complexity.xlsx',txt1,'Sheet1','F1');
这段代码是用 MATLAB 编写的,主要功能是将数据写入 Excel 文件中。具体来说,第一行代码将名为 city_complexity 的变量写入名为 A_science_city_complexity.xlsx 的 Excel 文件的 Sheet1 中的 F2 单元格中。第二行代码从名为 rca_2000.xlsx 的 Excel 文件的 Sheet1 中读取数据,并将其存储在 num、txt 和 AAA 三个变量中。第三行代码将 AAA 变量写入名为 A_science_city_complexity.xlsx 的 Excel 文件的 Sheet1 中的 A1 单元格中。第四行代码从名为 biaotou_city.xlsx 的 Excel 文件的 Sheet1 中读取数据,并将其存储在 txt1 变量中。最后一行代码将 txt1 变量写入名为 A_science_city_complexity.xlsx 的 Excel 文件的 Sheet1 中的 F1 单元格中。
D1 = xlsread('data.xlsx',1,'B2:Y25');
As an AI language model, I cannot access local files, but I can explain what this line of code does in MATLAB.
The function 'xlsread' in MATLAB is used to read numerical data from Excel spreadsheets.
The first argument 'data.xlsx' specifies the name of the Excel file that is being read.
The second argument '1' specifies the sheet number or name to read from. In this case, it is the first sheet.
The third argument 'B2:Y25' specifies the range of cells to read in the form of 'startcell:endcell'.
The output of this line of code is a matrix 'D1' that contains the numerical values from the specified range of cells in the Excel file.
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