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Fuzzy logic

What is fuzzy logic?

Fuzzy logic is a computing approach that deals with varying degrees of truth, rather than a “completely true or completely false” approach. The binary true or false approach is known as Boolean logic. Fuzzy logic is used when the situation is vague.

In the 1960s, Lofti Zadeh of the University of California at Berkeley was working on getting computers to understand natural language. He realized that natural language, like many other things in life, could not be translated into absolute terms of 1 (True) and 0 (False).

He figured that traditional computer logic could not handle vague data. He realized that there are many values between True and False that traditional computer logic did not consider. These could include:

  • Definitely true
  • Possible true
  • Can’t say
  • Possible false
  • Definitely false

With this in mind, Lofti Zadeh first described Fuzzy Logic in 1965. Fuzzy Logic makes decisions in a similar fashion as humans but does it much faster than we humans could.


Advantages of Fuzzy logic in AI

  • It does not require precise inputs - it can handle vague or distorted inputs.
  • Feedback sensors can be reprogrammed situationally.
  • The algorithms do not occupy large memory space.
  • The systems can use vague inputs to solve complex problems, similar to the way humans do.
  • Fuzzy logic systems are flexible and can have their rules modified.
  • These systems can make use of inexpensive sensors, reducing system costs.
  • Their structure is simple and they are very easy to construct.


Disadvantages of Fuzzy logic in AI

  • Since they work with vague data and inputs, these systems might not be very accurate.
  • They depend on human knowledge and expertise.
  • Their rules need to be updated on a regular basis.
  • To validate Fuzzy logic systems, they need to undergo a huge amount of testing.
  • Fuzzy logic systems cannot recognize neural networks or machine learning.
  • Fuzzy logic may present too many possible solutions for a problem, causing ambiguity.



Architecture of a Fuzzy logic system

Rule base

This contains the set of rules and conditions given to the system by the human experts. Recent developments have helped reduce the number of fuzzy rules required.


Fuzzification

Fuzzification is used to transform crisp inputs into fuzzy sets. Crisp inputs refer to exact inputs that are measured by sensors and sent to the fuzzy system for processing.


Inference engine

The inference engine determines the degree to which the fuzzy inputs match each rule. Based on this, it determines which rule needs to be fired, and the fired rules are then combined to form the control actions.


Defuzzification

Defuzzification transforms the fuzzy sets that the inference engine obtains into crisp values. 


Applications of Fuzzy Logic

Fuzzy logic is used in a range of ways. Here are some of the domains in which it is employed:

  • Transportation - Fuzzy logic is used here for vehicle speed control and traffic control. It is even used to control train schedules.
  • Medicine - It is used in diagnostic radiology and diagnostic support systems.
  • Artificial Intelligence - It is used in Natural Language Processing (NLP) and has other applications in AI as well.
  • Enterprise decision support - It is used in decision support systems in large organizations.
  • Chemical industry - Fuzzy logic is used to control pH levels, drying, and chemical distillation process.

Along with these, there are many other applications of Fuzzy Logic in a variety of domains.

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Fuzzy logic

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is fuzzy logic?

Fuzzy logic is a computing approach that deals with varying degrees of truth, rather than a “completely true or completely false” approach. The binary true or false approach is known as Boolean logic. Fuzzy logic is used when the situation is vague.

In the 1960s, Lofti Zadeh of the University of California at Berkeley was working on getting computers to understand natural language. He realized that natural language, like many other things in life, could not be translated into absolute terms of 1 (True) and 0 (False).

He figured that traditional computer logic could not handle vague data. He realized that there are many values between True and False that traditional computer logic did not consider. These could include:

  • Definitely true
  • Possible true
  • Can’t say
  • Possible false
  • Definitely false

With this in mind, Lofti Zadeh first described Fuzzy Logic in 1965. Fuzzy Logic makes decisions in a similar fashion as humans but does it much faster than we humans could.


Advantages of Fuzzy logic in AI

  • It does not require precise inputs - it can handle vague or distorted inputs.
  • Feedback sensors can be reprogrammed situationally.
  • The algorithms do not occupy large memory space.
  • The systems can use vague inputs to solve complex problems, similar to the way humans do.
  • Fuzzy logic systems are flexible and can have their rules modified.
  • These systems can make use of inexpensive sensors, reducing system costs.
  • Their structure is simple and they are very easy to construct.


Disadvantages of Fuzzy logic in AI

  • Since they work with vague data and inputs, these systems might not be very accurate.
  • They depend on human knowledge and expertise.
  • Their rules need to be updated on a regular basis.
  • To validate Fuzzy logic systems, they need to undergo a huge amount of testing.
  • Fuzzy logic systems cannot recognize neural networks or machine learning.
  • Fuzzy logic may present too many possible solutions for a problem, causing ambiguity.



Architecture of a Fuzzy logic system

Rule base

This contains the set of rules and conditions given to the system by the human experts. Recent developments have helped reduce the number of fuzzy rules required.


Fuzzification

Fuzzification is used to transform crisp inputs into fuzzy sets. Crisp inputs refer to exact inputs that are measured by sensors and sent to the fuzzy system for processing.


Inference engine

The inference engine determines the degree to which the fuzzy inputs match each rule. Based on this, it determines which rule needs to be fired, and the fired rules are then combined to form the control actions.


Defuzzification

Defuzzification transforms the fuzzy sets that the inference engine obtains into crisp values. 


Applications of Fuzzy Logic

Fuzzy logic is used in a range of ways. Here are some of the domains in which it is employed:

  • Transportation - Fuzzy logic is used here for vehicle speed control and traffic control. It is even used to control train schedules.
  • Medicine - It is used in diagnostic radiology and diagnostic support systems.
  • Artificial Intelligence - It is used in Natural Language Processing (NLP) and has other applications in AI as well.
  • Enterprise decision support - It is used in decision support systems in large organizations.
  • Chemical industry - Fuzzy logic is used to control pH levels, drying, and chemical distillation process.

Along with these, there are many other applications of Fuzzy Logic in a variety of domains.

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