What Is Fuzzy Logic?

2 Tutorial
At this point, you can use the optimization capability of anfis to improve the
model. First, try using a relatively short anfis training (20 epochs) without
implementing the checking data option, and then test the resulting FIS model
against the testing data. To perform the optimization, type the following
command:
fismat2=anfis([datin datout],fismat,[20 0 0.1]);
Here, 20 is the number of epochs, 0 is the training error goal, and 0.1 is the
initial step size.
This command returns the following result:
ANFIS info:
Number of nodes: 44
Number of linear parameters: 18
Number of nonlinear parameters: 30
Total number of parameters: 48
Number of training data pairs: 75
Number of checking data pairs: 0
Number of fuzzy rules: 3
Start training ANFIS ...
1 0.527607
.
.
20 0.420275
Designated epoch number reached --> ANFIS training completed at epoch 20
After the training is done, validate the model by typing the following
commands:
fuzout2=evalfis(datin,fismat2);
trnRMSE2=norm(fuzout2-datout)/sqrt(length(fuzout2))
chkfuzout2=evalfis(chkdatin,fismat2);
chkRMSE2=norm(chkfuzout2-chkdatout)/sqrt(length(chkfuzout2))
These commands return the following results:
2-152