A Guide To The Diverse Applications Of Fuzzy Logic

By Indeed Editorial Team

Published 3 April 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A fuzzy logic system is an approach that resembles human reasoning for issues that may not have absolute answers like a yes or no but considers the possibility of intermediate answers. It directly contrasts the boolean system, where 1 represents a truth value and 0 represents a false value. Learning about fuzzy logic can help you understand its various applications and usage. In this article, we examine some applications of fuzzy logic and its advantages.

What Are The Different Applications Of Fuzzy Logic?

You can find applications of fuzzy logic in advanced fields of science, technology and business. Lofti A. Zadeh introduced fuzzy sets in 1965 as a non-linear mathematical model to solve complex and non-linear problems. Wherever conventional logic technologies prove ineffective, fuzzy logic can help find accurate solutions. Fuzzy logic is a supplementary approach to conventional technologies. The most common application of fuzzy logic is in the field of control-systems.

In instances where engineers are unable to find accurate reasoning, fuzzy logic may enable them to generate inferences and proceed. Fuzzy logic helps with decision-making protocols in many industrial sectors. Fuzzy logic has huge potential in device control and mechatronics. Fuzzy logic exists in our day-to-day lives in the form of many applications. Control systems engineering, robotics, home appliances and electronics, healthcare, education, defence, finance and business management sectors use fuzzy logic-based applications. With the increasing prevalence of AI, fuzzy logic's popularity and usage is increasing correspondingly. Some common fuzzy logic applications, in various industries and systems include:

Aviation and aerospace industry

The aviation and aerospace industry faces problems related to regulation, planning and flight control. Fuzzy logic helps reduce risks and enables engineers to design complex and advanced control techniques in aircraft and spacecraft. Some prominent fuzzy logic applications in aviation and aerospace pertain to:

  • Engine control

  • Altitude control

  • Stability augmentation

  • Automatic flight control

  • Longitudinal and lateral directional motion control

  • Take-off and landing prediction

  • Satellite altitude control

  • Flow and mixture regulation

Related: What Is Control System Engineering? (With Career Advice)


Defence experts use fuzzy logic to create artificial intelligence-based systems that operate in complex battle environments. Fuzzy logic has several applications in defence, as many military operations involve environments which are vague or unclear. Defence uses fuzzy logic-based artificial intelligence systems for military operations which depend heavily on precision. The primary goal is to improve the effectiveness of the combat forces and reduce collateral damage. Some common fuzzy logic applications in defence include:

  • Mobility

  • Cybersecurity

  • Target tracking

  • Interceptor controls

  • Interoperability

  • Automated target recognition

  • Unmanned combat aerial vehicle control

  • Automated thermal infrared image recognition

  • Decision making

  • Unmanned helicopters

Related: A Complete Guide On How To Get An Army Job In India

Automotive industry

The automotive industry uses the latest technology and tools to improve safety, offer reliability and ensure comfort. Fuzzy logic is beneficial in automotive engineering as it can help tackle many complex engineering issues and reduce manufacturing costs. Some areas where fuzzy logic has been useful in the automobile industry include:

  • Anti-lock braking system

  • Anti-slip regulation

  • Traction control system

  • Active front steering

  • Automatic air conditioning systems

  • Automotive energy management systems

  • Speed control

  • Shift scheduling

  • Traffic control

Related: What Is Automotive Engineering? Definition And Career Tips

Business and finance

Fuzzy logic applications are one of the many methods or approaches businesses use to support decision-making. These applications can help reduce expenses by tracking uncertainties and using approximate data. Fuzzy logic in business applications use the reasoning that humans may face many uncertain situations yet can make the right decisions. Businesses can reduce considerable risk of loss as they constantly make decisions based on uncertain parameters and fuzzy logic is an approach tolerant to uncertainty. It can also reduce errors, save time and prevent human failures. Some common fuzzy logic applications in business and trading include:

  • Trading

  • Risk analysis and assessment

  • Financial management

  • Financial forecasting

  • Bankruptcy prediction

  • Claim management

  • Smart banking

  • Consumer credit score evaluation

  • Customer behaviour evaluation

  • Employee performance evaluation


Fuzzy logic techniques work alongside advanced control techniques in automation systems. It is used in places or processes where traditional systems cannot take into account exceptions. Fuzzy logic systems can take into account multiple variables simultaneously and help in better process control. Fuzzy logic is easy to implement in real-life situations. It is used in many electrical and electronic household appliances and industrial machinery applications. Some common applications of fuzzy logic in electronics include:

  • Washing machines

  • Air conditioners

  • Vacuum cleaners

  • Microwaves

  • Humidifiers

  • Video cameras

  • Refrigerators

Manufacturing and industrial sectors

Industries adapt to fuzzy logic systems to reduce manufacturing time and deployment cost in comparison to traditional approaches. Manufacturers use fuzzy logic to optimise productivity, improve quality and control output. Some complex fuzzy logic applications in the manufacturing industry include:

  • Kiln control

  • Steam turbine control

  • Wastewater management

  • Dairy and food production

  • Electrical discharge machining

  • Surface grinding

  • Metal spray formation

  • Energy utilisation forecasting


The typical focus of professionals in robotics is to make robots move, respond and interact without human intervention. Modern day robots can move autonomously and control their behaviour through sensor feedback mechanisms that help them perceive their surroundings. Robotics engineers combine artificial intelligence, fuzzy logic, advanced software and hardware for this purpose. Fuzzy logic can help robots navigate unpredictable environments. Some examples of applications of fuzzy logic in robotics include:

  • Intelligent wheelchairs

  • Intelligent robots

  • Humanoid robots

  • Industrial robots

  • Medical and surgical robots

Related: What Are The Goals Of Artificial Intelligence? (And Methods)


The role of artificial intelligence and fuzzy logic in diagnosing diseases and treating them is increasing steadily. Fuzzy logic based algorithms help in the early diagnosis of diseases like cancer, which helps doctors treat patients early on to prevent advanced complications. Surgeons use artificial intelligence and surgical robots to perform laparoscopic and complex cardiac and spinal surgeries. Some expert systems in healthcare that use fuzzy logic are:

  • Applications that determine the risk of a specific disease like cancer

  • Applications that monitor a patient's condition during a surgery

  • Applications to determine anaemia

  • Applications that determine drug dosage

  • Applications that control operation theatre environment

  • Applications that create high definition and precise imaging tests


Transport managers and city administrators use fuzzy logic and mathematical modelling to solve routine traffic and transportation problems. Fuzzy logic is very helpful in this domain, as it can incorporate multiple uncertainties like sudden traffic jams, congestion, signal problems, accidents or any disruption in the flow of traffic. Railways use fuzzy logic to streamline operations and optimise route utilisation and revenue generation. Ticketing websites, tour and travel agents, GPS companies, fleet operators and logistic companies benefit from applications that combine artificial intelligence and fuzzy logic. Fuzzy logic applications in transportation include:

  • Trip distribution

  • Traffic flow evaluation

  • Modal split

  • Route choice

  • Traffic assignment

  • Travel time estimation

  • Shortest path identification

  • Underground railroad operation

  • Train, flight and bus scheduling protocols

Related: How To Become A Transportation Engineer: A Complete Guide

Facial recognition

Fuzzy logic-based applications may be able to recognise human faces and similar imagery with 80% accuracy. It can be successful in controlled and uncontrolled environments using facial extraction and landmark point identification. An example of facial recognition is when a social media platform identifies a person from a video clip for tagging. Fuzzy logic applications in facial recognition assist:

  • Law enforcement agencies

  • Passport agencies

  • Border patrol agencies

  • Banks and financial institutions

  • Schools, hotels, spas, gyms and private clubs for biometric authentication

  • Home and automobile security systems


AI and fuzzy logic-based applications offer many tools and expert analyses for psychological testing and diagnosis. These technologies can combine data mining and deep analysis to diagnose current and potential problems. Such tools can also help professionals formulate and create tests, confirm a diagnosis and prescribe treatment plans. Fuzzy logic applications in psychology include:

  • Diagnosis of diseases like depression

  • Identification of suicidal tendencies

  • Treatment of anxiety disorders

  • Diagnosis of autism

  • Diagnosis of mood disorders

Related: 12 Common Data Mining Interview Questions (With Answers)


Fuzzy logic-based techniques and approaches have proven useful to evaluate the performance of teachers and students. These techniques consider many measurable and tangible parameters like knowledge, creativity, discipline and performance. It can measure the success or failure of teaching methodologies and their impact on students. Fuzzy logic helps draw inferences and conclusions for improving the quality of educational practices. Popular fuzzy logic applications in education include:

  • Evaluating the performance of students

  • Evaluating the performance of faculty

  • Ranking and grading students

  • Choosing ideal assignments and tasks

  • Translating grades into percentages

  • Testing assignments and exams

What Are The Advantages Of Using Fuzzy Logic-Based Systems?

As per the conventional Boolean logic, a computer can only produce results that are true or false. If there is a problem with vague answers that are not a clear yes or no, the Boolean logic may not give accurate solutions. The main advantage of fuzzy logic is that, similar to human problem-solving thought, it can consider solutions to problems with no clear answers. Apart from this aspect of this logic system, there are other benefits like:

  • It is flexible and adaptable.

  • It is easy to construct fuzzy logic systems even for complex solutions.

  • The logic is robust, simple and allows customisation.

  • Developers can code with a lesser amount of data.

Related: 8 Artificial Intelligence Examples In Different Industries

Explore more articles