ANFIS implementation In Matlab

ANIFS:

The acronym ANFIS derives its name from adaptive neuro-fuzzy inference system. Using a given input/output data set, the toolbox function 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 are modeling. 

For ANFIS Editor GUI, just type anfisedit on the command-line of Matlab.

Training, testing, or checking data are loaded from disk or workspace or demo data. Data (training, testing, and checking) is loaded by selecting appropriate radio buttons in the Load data portion of the GUI and then clicking Load Data... The loaded data is plotted on the plot region.

Testing data appears on the plot in blue as . . ; Training data appears on the plot in blue as o o ; Checking data appears on the plot in blue as ++ ; FIS output appears on the plot in red as **.

Training Data

 

Checking Data

 

Load FIS or generate FIS from loaded data using your chosen number of MFs and rules or fuzzy.

 

The fuzzy membership functions and/or rules set initially can also be modified by selecting the edit menu from the top left screen of the anfis editor.

 

 

After generating or loading a FIS, the structure button allows you to open a graphical representation of its input/output structure.

Generated ANFIS:

 

Afterwards, choose the FIS model parameter optimization method: Back-propagation or a mixture of back-propagation and least squares (hybrid method). Also choose the number of training epochs and the training error tolerance.

FIS model is trained by clicking the Train Now button. This training adjusts the membership function parameters and plots the training (and/or checking data) error plot(s) in the plot region.

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FIS model output versus the training, checking, or testing data output is viewed by clicking the Test Now button.