In this tutorial, you will learn
What Is Fuzzy Logic?
History of Fuzzy Logic Systems
Characteristics of Fuzzy Logic
When not to use fuzzy logic
Fuzzy Logic Architecture
Fuzzy Logic vs. Probability
Crisp vs. Fuzzy
Classical Set vs. Fuzzy set Theory
Fuzzy Logic Examples
Application Areas of Fuzzy Logic
Advantages of Fuzzy Logic System
Disadvantages of Fuzzy Logic Systems
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:
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
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:
If you don’t find it convenient to map an input space to an output space Fuzzy logic should not be used when you can use common sense Many controllers can do the fine job without the use of fuzzy logic
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
Crisp vs. Fuzzy
Classical Set vs. Fuzzy set Theory
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.
Application Areas of Fuzzy Logic
The Blow given table shows application of Fuzzy logic by famous companies in their products.
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 Fuzzy logic in AI helps you to control machines and consumer products It may not offer accurate reasoning, but the only acceptable reasoning Fuzzy logic in Data Mining 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
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
Summary
The term fuzzy mean things which are not very clear or vague The term fuzzy logic was first used with 1965 by Lotfi Zadeh a professor of UC Berkeley in California Fuzzy logic is a flexible and easy to implement machine learning technique Fuzzy logic should not be used when you can use common sense Fuzzy Logic architecture has four main parts 1) Rule Basse 2) Fuzzification 3) Inference Engine 4) Defuzzification Fuzzy logic takes truth degrees as a mathematical basis on the model of the vagueness while probability is a mathematical model of ignorance Crisp set has strict boundary T or F while Fuzzy boundary with a degree of membership A classical set is widely used in digital system design while fuzzy set Used only in fuzzy controllers Auto transmission, Fitness management, Golf diagnostic system, Dishwasher, Copy machine are some areas of Fuzzy Logic applications Fuzzy logic in Soft Computing helps you to control machines and consumer products
