Fuzzy logic is a form of knowledge representation that allows for notions without precise definitions. It was introduced in 1965 by Lotfi Zadeh as an extension of traditional binary sets to fuzzy sets where elements can belong to multiple sets with varying degrees of membership. Fuzzy logic uses fuzzy sets that assign membership degrees between 0 and 1 rather than crisp 0 or 1 values, allowing for flexible and easy implementation of machine learning techniques to mimic human reasoning. It has been applied in various consumer products and industrial systems such as anti-lock brakes, auto transmissions, copy machines, and cruise control.

1. FUZZY LOGIC
SUMERA HANGI
ROLL N0 13

2. WHAT IS FUZZY LOGIC? & WHERE DID IT
BEGIN?
 What is fuzzy ?
Fuzzy – “Not Clear, Distinct, Or Precise; Blurred”
 Definition Of Fuzzy Logic
 A Form Of Knowledge Representation Suitable For Notions That Cannot Be
Defined Precisely, But Which Depend Upon Their Contexts.
 The Term Fuzzy Logic Was Introduced With The 1965 Proposal Of Fuzzy Set
Theory By Lotfi Zadeh.
Artificial Inteligence Rollno 13

3. Characteristics of Fuzzy Logic
Characteristics of Fuzzy Logic
 Flexible and easy to implement machine learning technique
 Helps you to mimic the logic of human thought
 Logic may have two values which represent two possible solutions
 Highly suitable method for uncertain or approximate reasoning
 Fuzzy logic views inference as a process of propagating elastic constraints
 Fuzzy logic allows you to build nonlinear functions of arbitrary complexity.
 Fuzzy logic should be built with the complete guidance of experts
Artificial Inteligence Rollno 13

4. Fuzzy Logic Architecture
Artificial Inteligence Rollno 13

5. TRADITIONAL REPRESENTATION OF LOGIC
Slow Fast
Speed = 0 Speed = 1
bool speed; get the speed
if ( speed == 0)
{
// speed is slow
} else {
// speed is fast
}
Artificial Inteligence Rollno 13

6. FUZZY LOGIC REPRESENTATION
 For Every Problem Must Represent In Terms Of Fuzzy Sets.
 What Are Fuzzy Sets?
Slowest
[ 0.0 – 0.25 ]
Slow
[ 0.25 – 0.50 ]
Fast
[ 0.50 – 0.75 ]
Fastest
[ 0.75 – 1.00 ]
Artificial Inteligence Rollno 13

7. float speed; get the speed
if ((speed >= 0.0)&&(speed < 0.25)) {
// speed is slowest }
else if ((speed >= 0.25)&&(speed < 0.5))
{ // speed is slow }
else if ((speed >= 0.5)&&(speed < 0.75))
{ // speed is fast }
else // speed >= 0.75 && speed < 1.0
{ // speed is fastest }
Artificial Inteligence Rollno 13

8. Fuzzy Logic Examples
See the below-given diagram. It shows that in fuzzy systems, the values are
denoted by a 0 to 1 number. In this example, 1.0 means absolute truth and
0.0 means absolute falseness.
Artificial Inteligence Rollno 13

9. Application Areas of Fuzzy Logic
Product Company
Artificial Inteligence Rollno 13
Anti-lock brakes Nissan
Auto transmission NOK/Nissan
Auto engine Honda, Nissan
Copy machine Canon
Cruise control Nissan, Isuzu, Mitsubishi
Dishwasher Matsushita
Elevator control Fujitec, Mitsubishi Electric,
Toshiba
Golf diagnostic system Maruman Golf
Fitness management Omron

10. Artificial Inteligence Rollno 13

11. Advantages of Fuzzy Logic System
 The structure of Fuzzy Logic Systems is easy and understandable
 Fuzzy logic is widely used for commercial and practical purposes
 It helps you to control machines and consumer products
 It may not offer accurate reasoning, but the only acceptable reasoning
 It helps you to deal with the uncertainty in engineering
 Mostly robust as no precise inputs required
 It can be programmed to in the situation when feedback sensor stops working
 It can easily be modified to improve or alter system performance
 inexpensive sensors can be used which helps you to keep the overall system cost
and complexity low
 It provides a most effective solution to complex issues
Artificial Inteligence Rollno 13

12. BENEFITS OF USING FUZZY LOGIC
Artificial Inteligence Rollno 13

13. Disadvantages of Fuzzy Logic Systems
 Fuzzy logic is not always accurate, so The results are perceived based on
assumption, so it may not be widely accepted.
 Fuzzy systems don't have the capability of machine learning as-well-as neural
network type pattern recognition
 Validation and Verification of a fuzzy knowledge-based system needs
extensive testing with hardware
 Setting exact, fuzzy rules and, membership functions is a difficult task
 Some fuzzy time logic is confused with probability theory and the terms
Artificial Inteligence Rollno 13

14. Artificial Inteligence Rollno 13