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.