Fuzzy Logic Tutorial: What is, Architecture, Application, Example

Details

What Is Fuzzy Logic?

Fuzzy Logic is defined as a many-valued logic form which may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may come across a situation where we can't decide whether the statement is true or false. At that time, fuzzy logic offers very valuable flexibility for reasoning.

Fuzzy logic algorithm helps to solve a problem after considering all available data. Then it takes the best possible decision for the given the input. The FL method imitates the way of decision making in a human which consider all the possibilities between digital values T and F.

In this tutorial, you will learn

History of Fuzzy Logic Systems

Although, the concept of fuzzy logic had been studied since the 1920's. The term fuzzy logic was first used with 1965 by Lotfi Zadeh a professor of UC Berkeley in California. He observed that conventional computer logic was not capable of manipulating data representing subjective or unclear human ideas.

Fuzzy algorithm has been applied to various fields, from control theory to AI. It was designed to allow the computer to determine the distinctions among data which is neither true nor false. Something similar to the process of human reasoning. Like Little dark, Some brightness, etc.

Characteristics of Fuzzy Logic

Here, are some important characteristics of fuzzy logic:

When not to use fuzzy logic

However, fuzzy logic is never a cure for all. Therefore, it is equally important to understand that where we should not use fuzzy logic.

Here, are certain situations when you better not use Fuzzy Logic:

Fuzzy Logic Architecture

Fuzzy Logic Architecture

Fuzzy Logic Architecture

Fuzzy Logic architecture has four main parts as shown in the diagram:

Rule Base:

It contains all the rules and the if-then conditions offered by the experts to control the decision-making system. The recent update in fuzzy theory provides various methods for the design and tuning of fuzzy controllers. This updates significantly reduce the number of the fuzzy set of rules.

Fuzzification:

Fuzzification step helps to convert inputs. It allows you to convert, crisp numbers into fuzzy sets. Crisp inputs measured by sensors and passed into the control system for further processing. Like Room temperature, pressure, etc.

Inference Engine:

It helps you to determines the degree of match between fuzzy input and the rules. Based on the % match, it determines which rules need implment according to the given input field. After this, the applied rules are combined to develop the control actions.

Defuzzification:

At last the Defuzzification process is performed to convert the fuzzy sets into a crisp value. There are many types of techniques available, so you need to select it which is best suited when it is used with an expert system.

Fuzzy Logic vs. Probability

Fuzzy LogicProbability
Fuzzy: Tom's degree of membership within the set of old people is 0.90. Probability: There is a 90% chance that Tom is old.
Fuzzy logic takes truth degrees as a mathematical basis on the model of the vagueness phenomenon. Probability is a mathematical model of ignorance.

Crisp vs. Fuzzy

CrispFuzzy
It has strict boundary T or F Fuzzy boundary with a degree of membership
Some crisp time set can be fuzzy It can't be crisp
True/False {0,1} Membership values on [0,1]
In Crisp logic law of Excluded Middle and Non- Contradiction may or may not hold In the fuzzy logic law of Excluded Middle and Non- Contradiction hold

Classical Set vs. Fuzzy set Theory

Classical SetFuzzy Set Theory
Classes of objects with sharp boundaries. Classes of objects do not have sharp boundaries.
A classical set is defined by crisp boundaries, i.e., there is clarity about the location of the set boundaries. A fuzzy set always has ambiguous boundaries, i.e., there may be uncertainty about the location of the set boundaries.
Widely used in digital system design Used only in fuzzy controllers.

Fuzzy Logic Examples

See the below-given diagram. It shows that in a Fuzzy system, the values are denoted by a 0 to 1 number. In this example, 1.0 means absolute truth and 0.0 means absolute falseness.

Fuzzy Logic with Example

Fuzzy Logic with Example

Application Areas of Fuzzy Logic

The Blow given table shows application of Fuzzy logic by famous companies in their products.

ProductCompanyFuzzy Logic
Anti-lock brakes Nissan Use fuzzy logic to controls brakes in hazardous cases depend on car speed, acceleration, wheel speed, and acceleration
Auto transmission NOK/Nissan Fuzzy logic is used to control the fuel injection and ignition based on throttle setting, cooling water temperature, RPM, etc.
Auto engine Honda, Nissan Use to select geat based on engine load, driving style, and road conditions.
Copy machine Canon Using for adjusting drum voltage based on picture density, humidity, and temperature.
Cruise control Nissan, Isuzu, Mitsubishi Use it to adjusts throttle setting to set car speed and acceleration
Dishwasher Matsushita Use for adjusting the cleaning cycle, rinse and wash strategies based depend upon the number of dishes and the amount of food served on the dishes.
Elevator control Fujitec, Mitsubishi Electric, Toshiba Use it to reduce waiting for time-based on passenger traffic
Golf diagnostic system Maruman Golf Selects golf club based on golfer's swing and physique.
Fitness management Omron Fuzzy rules implied by them to check the fitness of their employees.
Kiln control Nippon Steel Mixes cement
Microwave oven Mitsubishi Chemical Sets lunes power and cooking strategy
Palmtop computer Hitachi, Sharp, Sanyo, Toshiba Recognizes handwritten Kanji characters
Plasma etching Mitsubishi Electric Sets etch time and strategy

Advantages of Fuzzy Logic System

Disadvantages of Fuzzy Logic Systems

Summary

 

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