What Is Fuzzy Logic?

2 Tutorial
Overview
The point of fuzzy logic is to map an input space to an output space, and the
primary mechanism for doing this is a list of if-then statements called rules.
All rules are evaluated in parallel, and the order of the rules is unimportant.
The rules themselves are useful because they refer to variables and the
adjectives that describe those variables. Before you can build a system that
interprets rules, you must define all the terms you plan on using and the
adjectives that describe them. To say that the water is hot, you need to define
the range that the water’s temperature can be expected to vary as well as
what we mean by the word hot. The following diagram provides a roadmap for
the fuzzy inference process. It shows the general description of a fuzzy system
on the left and a specific fuzzy system (the tipping example from Chapter 1,
“Getting Started”) on the right.
The General Case
A Specific Example
Input
Output
service
tip
Rules
if service is poor then tip is cheap
if service is good then tip is average
if service is excellent then tip is generous
Input
terms
(interpret)
Output
terms
(assign)
service
is interpreted as
{poor,
good,
excellent}
tip
is assigned to be
{cheap,
average,
generous}
To summarize the concept of fuzzy inference depicted in this figure, fuzzy
inference is a method that interprets the values in the input vector and, based
on some set of rules, assigns values to the output vector.
This section is designed to guide you through the fuzzy logic process step by
step by providing an introduction to the theory and practice of fuzzy logic. The
first three sections of this section are the most important—they move from
general to specific, first introducing underlying ideas and then discussing
implementation details specific to the toolbox.
These three areas are as follows:
2-2