A few days ago, I experimented with the neural network toolbox in MATLAB and had a sudden feeling of being "stuck in a well." It wasn't that anything was wrong, but the data structure required by the neural network seemed a bit "weird," and if not handled carefully, it could lead to errors in the toolbox. Here's the correct way to use the neural network toolbox, for your reference:
One method I tried involves using the batch command to call the Neural Network Toolbox. Here's an example of how to set up the input and target data:
```matlab
P = [0.1515 0.1501 0.1509 0.1504 0.1504 0.1500
0.1515 0.1501 0.1509 0.1504 0.1504 0.1500
0.1515 0.1501 0.1500 0.1504 0.1504 0.1500
0.1515 0.1500 0.1509 0.1504 0.1504 0.1500
0.1515 0.1500 0.1509 0.1504 0.1504 0.1500
0.1515 0.1500 0.1509 0.1504 0.1504 0.1500
0.9684 0.2792 0.877 0.7426 0.7228 0.2272
0.9838 0.2941 0.9181 0.7977 0.7702 0.2452
0.9922 0.3101 0.9475 0.8445 0.8227 0.2665
0.9953 0.3058 0.9625 0.8708 0.8637 0.2624
0.9982 0.3242 0.9797 0.9089 0.9001 0.3008
0.9995 0.3469 0.9917 0.9314 0.9282 0.3678
0.9998 0.3565 0.9948 0.9493 0.9525 0.4500];
T = [0.1521
0.6949
0.7064
0.7083
0.7560
0.7807
0.8182
0.8533
0.8677
0.8459
0.8910
0.9269
0.9496];
P = P'; T = T';
ff = newff(P, T, 13);
ff.trainParam.epochs = 15000;
ff = train(ff, P, T);
Y1 = sim(ff, P);
cf = newcf(P, T, 13);
cf.trainParam.epochs = 15000;
cf = train(cf, P, T);
Y2 = sim(cf, P);
plot(P, T, 'o-');
hold on;
plot(P, Y1, '^m-');
plot(P, Y2, '*-k');
title('newff & newcf');
legend('original data', 'newff result', 'newcf result', 0);
```
It’s important to transpose `P` and `T` first, as each column in the matrix represents a training sample.
After installing MATLAB, open it and type `anfisedit` in the command window. This will bring up the Fuzzy Inference System (FIS) editor interface.
Click the “Load Data†button in the first red box to import your data. You can either load it from a file or from the workspace. The data format must be correct: the first part should be the system inputs, and the latter part should be the outputs. For example, if your system has 3 inputs and 1 output, the data should be structured as `[x1, x2, x3, y]`.
Once you’ve imported the training data (selecting the “Training†data type), the interface will update accordingly. Then, click on “FIS Properties†under the “Edit†menu to adjust the settings of your fuzzy system.
This setup helps ensure that your fuzzy neural network is properly configured and ready for training and testing.
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