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What is fuzzy logic in AI?

Jonathan Meagher

December 26, 2022

Fuzzy logic is a mathematical system that represents and manipulates uncertainty and imprecision in artificial intelligence (AI) and other fields. It is based on fuzzy sets, which simultaneously allow for the membership of elements in more than one category, with a degree of membership between 0 and 1.

Overview

 

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Fuzzy logic offers an extension to classical Boolean logic based on the principles of true or false, on or off, and 1 or 0. In contrast, fuzzy logic allows for the representation of partial truths. Values between 0 and 1 represent the degree of truth of a statement.

This allows the representation of concepts and ideas that are difficult to represent in classical Boolean logic. For example, experts can find it challenging to define “somewhat cold” or “slightly heavy” in Boolean logic.

In contrast to probability theory, fuzzy logic adds numeric values between o and 1 to each proposition to define uncertainty.

Probability theory measures how many chances a proposition is to be correct. But, fuzzy logic analyses the degree to which the proportion is correct.

Main Features Of Fuzzy Logic

 

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Fuzzy logic, a nonlinear system with more than one number used, helps in decision-making. It uses multidimensional data to define a rule or behavior rather than just one value or number.
The main features of fuzzy logic include the following:

  • Every statement may have more than one truth.
  • The use of many measurements rather than just one measurement (usually called “fuzzy set theory”).

For example, suppose you have three different measurements for how much money you have in your bank account. Then those three numbers could add up to higher than zero and lower than zero. They would be, therefore, considered “fuzzy” sets because they can’t be fully defined by either 0 or 100%.

Fuzzy Logic Applications

There are various applications of Fuzzy logic. Here are some of them:

Fuzzy Logic In Control Systems

Fuzzy control systems or fuzzy control logic allow experts to define their own rules. For example:- “If you are approaching the destination station and going fast, increase the train’s braking pressure.” Experts can define and refine vague rules in the system numerically.

Many of the first successful fuzzy logic applications were implemented in Japan’s Sendai Subway 1000 series. After the implementation, fuzzy logic improved the ride’s economy, comfort, and precision. Also, Fuzzy logic has provided some other successful applications like handwriting recognition in Sony Pocket computers, helicopter flight aids, improved car fuel efficiency, and control of subway systems.

Medical Decision Making And Fuzzy Logic

Fuzzy logic offers great value for medical decision-making. Since medical and health data can be subjective or fuzzy, applications in this area have great potential to gain a lot by using fuzzy logic-based approaches.

Fuzzy logic can work in different aspects of medical decision-making.

These include medical image analysis, biomedical signal analysis, image or signal segmentation, and feature extraction/selection from images or signals. However, fuzzy logic’s application in medical decision-making can be challenging. Medical areas have more elicit data from humans than any other field.

Fuzzy Logic And Image-Based Computer-Aided Diagnosis

One of the most common applications using fuzzy logic is the image-based computer-aided diagnosis (CAD) in healthcare. CAD is a computerized set of interconnected tools helping physicians make their diagnostic decisions.

For example, if a physician finds an abnormal lesion in a very early stage of development, they can use a CAD approach to characterize the lesion and diagnose its nature. Fuzzy logic may help describe the main features of this lesion.

Fuzzy Logic And Databases

Experts can design fuzzy relationships, and it is possible to develop fuzzy relational databases. Fuzzy query languages have been specified, such as FSQL by J. Galindo et al. and SQLf by P. Bosc et al.

These languages define some structures to include fuzzy aspects in SQL statements, such as fuzzy conditions, fuzzy constants, fuzzy constraints, fuzzy thresholds, fuzzy comparators, language labels, etc.

Talking Points From This Guide

Fuzzy logic to describe and classify certain types of uncertainties. It is a nonlinear system, meaning fuzzy logic uses rules in data analysis that do not hold exactly true or false values but rather somewhere between these two extremes.

Fuzzy logic has a number of applications in AI, particularly in the areas of expert systems and control systems. In expert systems, fuzzy logic can also represent the uncertainty and imprecision of human knowledge, allowing the system to reason about and make decisions based on that knowledge.

In control systems, fuzzy logic designs controllers that can adapt to changing conditions and respond appropriately to input.
Overall, fuzzy machine learning offers a powerful way to represent and manipulate uncertainty and imprecision in AI and other fields.

It allows for the representation of concepts and ideas difficult to represent in classical Boolean logic. Fuzzy logic offers solutions to a wide range of problems in fields such as engineering, medicine, and finance.

About The Author

Jonathan Meagher

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