The basic structure of Mamdani fuzzy inference system is a model that maps input characteristics to input membership functions, input membership functions to rules, rules to a set of output characteristics, output characteristics to output membership functions, and the output membership functions to a single-valued output or a decision associated with the output. Such a system uses fixed membership functions that are chosen arbitrarily and a rule structure that is essentially predetermined by the user's interpretation of the characteristics of the variables in the model.
anfis and the Neuro-Fuzzy Designer apply fuzzy inference techniques to data modeling.
As you have seen from the other fuzzy inference GUIs, the shape of the membership
functions depends on parameters, and changing these parameters change the shape of
the membership function. Instead of just looking at the data to choose the
membership function parameters, you choose membership function parameters
automatically using these Fuzzy Logic
Suppose you want to apply fuzzy inference to a system for which you already have a collection of input/output data that you would like to use for modeling, model-following, or some similar scenario. You do not necessarily have a predetermined model structure based on characteristics of variables in your system.
In some modeling situations, you cannot discern what the membership functions
should look like simply from looking at data. Rather than choosing the parameters
associated with a given membership function arbitrarily, these parameters could be
chosen so as to tailor the membership functions to the input/output data in order to
account for these types of variations in the data values. In such cases, you can use
the Fuzzy Logic
neuro-adaptive learning techniques incorporated in the
The neuro-adaptive learning method works similarly to that of neural networks.
Neuro-adaptive learning techniques provide a method for the fuzzy modeling procedure
to learn information about a data set. Fuzzy Logic
Toolbox software computes the membership function parameters that best allow
the associated fuzzy inference system to track the given input/output data. The
Toolbox function that accomplishes this membership function parameter
adjustment is called
function can be accessed either from the command line or through the
Neuro-Fuzzy Designer. Because the functionality of the command line
anfis and the Neuro-Fuzzy Designer is
similar, they are used somewhat interchangeably in this discussion, except when
specifically describing the Neuro-Fuzzy Designer app.
The acronym ANFIS derives its name from adaptive neuro-fuzzy
inference system. Using a given input/output data set, the
anfis constructs a fuzzy inference system
(FIS) whose membership function parameters are tuned (adjusted) using either a
back propagation algorithm alone or in combination with a least squares type of
method. This adjustment allows your fuzzy systems to learn from the data they
A network-type structure similar to that of a neural network, which maps inputs through input membership functions and associated parameters, and then through output membership functions and associated parameters to outputs, can be used to interpret the input/output map.
The parameters associated with the membership functions changes through the
learning process. The computation of these parameters (or their adjustment) is
facilitated by a gradient vector. This gradient vector provides a measure of how
well the fuzzy inference system is modeling the input/output data for a given
set of parameters. When the gradient vector is obtained, any of several
optimization routines can be applied in order to adjust the parameters to reduce
some error measure. This error measure is usually defined by the sum of the
squared difference between actual and desired outputs.
anfis uses either back propagation or a combination of
least squares estimation and back propagation for membership function parameter
The modeling approach used by
anfis is similar to many
system identification techniques. First, you hypothesize a parameterized model
structure (relating inputs to membership functions to rules to outputs to
membership functions, and so on). Next, you collect input/output data in a form
that will be usable by
anfis for training. You can then use
anfis to train the FIS model to
emulate the training data presented to it by modifying the membership function
parameters according to a chosen error criterion.
In general, this type of modeling works well if the training data presented to
anfis for training (estimating) membership function
parameters is fully representative of the features of the data that the trained
FIS is intended to model. In some cases however, data is collected using noisy
measurements, and the training data cannot be representative of all the features
of the data that will be presented to the model. In such situations, model
validation is helpful.
Model Validation Using Testing and Checking Data Sets. Model validation is the process by which the input vectors from input/output data sets on which the FIS was not trained, are presented to the trained FIS model, to see how well the FIS model predicts the corresponding data set output values.
One problem with model validation for models constructed using adaptive techniques is selecting a data set that is both representative of the data the trained model is intended to emulate, yet sufficiently distinct from the training data set so as not to render the validation process trivial.
If you have collected a large amount of data, hopefully this data contains all the necessary representative features, so the process of selecting a data set for checking or testing purposes is made easier. However, if you expect to be presenting noisy measurements to your model, it is possible the training data set does not include all of the representative features you want to model.
The testing data set lets you check the generalization capability of the resulting fuzzy inference system. The idea behind using a checking data set for model validation is that after a certain point in the training, the model begins overfitting the training data set. In principle, the model error for the checking data set tends to decrease as the training takes place up to the point that overfitting begins, and then the model error for the checking data suddenly increases. Overfitting is accounted for by testing the FIS trained on the training data against the checking data, and choosing the membership function parameters to be those associated with the minimum checking error if these errors indicate model overfitting.
Usually, these training and checking data sets are collected based on observations of the target system and are then stored in separate files.
In the first example, two similar data sets are used for checking and
training, but the checking data set is corrupted by a small amount of noise.
This example illustrates of the use of the Neuro-Fuzzy Designer
with checking data to reduce the effect of model overfitting. In the second
example, a training data set that is presented to
is sufficiently different than the applied checking data set. By examining
the checking error sequence over the training period, it is clear that the
checking data set is not good for model validation purposes. This example
illustrates the use of the Neuro-Fuzzy Designer to compare data
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