What Is Fuzzy Logic?

2 Tutorial
anfis and the ANFIS Editor GUI
In this section...
“Introduction” on page 2-106
“A Modeling Scenario” on page 2-107
“Model Learning and Inference Through ANFIS” on page 2-107
“Know Your Data” on page 2-108
“Constraints of anfis” on page 2-110
“Training Adaptive Neuro Fuzzy Inference Systems Using the ANFIS
Editor GUI” on page 2-110
“ANFIS Editor GUI Example 1: Checking Data Helps Model Validation” on
page 2-114
“ANFIS Editor GUI Example 2: Checking Data Does Not Validate Model”
on page 2-124
“anfis from the Command Line” on page 2-128
“More on anfis and the ANFIS Editor GUI” on page 2-134
Introduction
The basic structure of the type of fuzzy inference system seen thus far is a
model that maps input characteristics to input membership functions, input
membership function to rules, rules to a set of output characteristics, output
characteristics to output membership functions, and the output membership
function to a single-valued output or a decision associated with the output.
You have considered only fixed membership functions that were chosen
arbitrarily. You have applied fuzzy inference to only modeling systems whose
rule structure is essentially predetermined by the user’s interpretation of the
characteristics of the variables in the model.
In this section we discuss the use of the function anfis and the ANFIS
Editor GUI in the toolbox. These tools 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
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