Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Tune Sugeno-type fuzzy inference system using training data

`fis = anfis(trainingData)`

`fis = anfis(trainingData,options)`

```
[fis,trainError]
= anfis(___)
```

```
[fis,trainError,stepSize]
= anfis(___)
```

```
[fis,trainError,stepSize,chkFIS,chkError]
= anfis(trainingData,options)
```

generates a single-output Sugeno fuzzy inference system (FIS) and tunes the
system parameters using the specified input/output training data. The FIS object
is automatically generated using grid partitioning.`fis`

= anfis(`trainingData`

)

The training algorithm uses a combination of the least-squares and backpropagation gradient descent methods to model the training data set.

tunes
an FIS using the specified training data and options. Using this syntax,
you can specify:`fis`

= anfis(`trainingData`

,`options`

)

An initial FIS object to tune.

Validation data for preventing overfitting to training data.

Training algorithm options.

Whether to display training progress information.

`[`

returns the root mean square training
error for each training epoch.`fis`

,`trainError`

]
= anfis(___)

`[`

returns the training step size
at each training epoch.`fis`

,`trainError`

,`stepSize`

]
= anfis(___)

`[`

returns the validation data error for each training epoch,
`fis`

,`trainError`

,`stepSize`

,`chkFIS`

,`chkError`

]
= anfis(`trainingData`

,`options`

)`chkError`

, and the tuned FIS object for which the
validation error is minimum, `chkFIS`

. To use this syntax,
you must specify validation data using
`options.ValidationData`

.

[1] Jang, J.-S. R., "Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter
Algorithm," *Proc. of the Ninth National Conf. on Artificial Intelligence
(AAAI-91)*. July 1991, pp. 762-767.

[2] Jang, J.-S. R., "ANFIS: Adaptive-Network-based Fuzzy Inference Systems,"
*IEEE Transactions on Systems, Man, and Cybernetics*, Vol. 23,
No. 3, May 1993, pp. 665-685.