What Is Fuzzy Logic?

anfis
Purpose
Syntax
Description
Training routine for Sugeno-type Fuzzy Inference System (MEX only)
[fis,error,stepsize] = anfis(trnData)
[fis,error,stepsize] = anfis(trnData,initFis)
[fis,error,stepsize] = anfis(trnData,numMFs)
[fis,error,stepsize,chkFis,chkErr] = ...
anfis(trnData,initFis,trnOpt,dispOpt,chkData,optMethod)
[fis,error,stepsize,chkFis,chkErr] = ...
anfis(trnData,numMFs,trnOpt,dispOpt,chkData,optMethod)
This syntax is the major training routine for Sugeno-type fuzzy inference
systems. anfis uses a hybrid learning algorithm to identify parameters
of Sugeno-type fuzzy inference systems. It applies a combination of
the least-squares method and the backpropagation gradient descent
method for training FIS membership function parameters to emulate a
given training data set. anfis canalsobeinvokedusinganoptional
argument for model validation. The type of model validation that takes
place with this option is a checking for model overfitting, and the
argument is a data set called the checking data set.
The arguments in the description for anfis are as follows. Note
that you can specify the arguments trnOpt, dispOpt, chkData, and
optMethod as empty, [], when necessary:
• trnData: the name of a training data set. This matrix contains data
input in all but the last column. The last column contains a single
vector of output data.
• initFis: the name of a fuzzy inference system (FIS) used to provide
anfis with an initial set of membership functions for training.
Without this option, anfis uses genfis1 to implement a default
initial FIS for training. This default FIS has two membership
functions of the Gaussian type, when it is invoked with only one
argument. If initFis is provided as a single number (or a vector),
it is taken as the number of membership functions (or the vector)
whose entries are the respective numbers of membership functions
associated with each respective input when these numbers differ
for each input). In this case, both arguments of anfis are passed
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