Fuzzy logic is way of computing based on degree of truth rather than the binary truth values. it is used to create and manipulate fuzzy sets. Its a mathematical language to deal with the fuzzy representation of real world problems. Fuzzy sets are extension of crisp sets. Crisp sets are used to represent boolean valued entity, fuzzy sets are used to represent multivalued entity.

Fuzzy sets and logic are widely used in the design of fuzzy logic controller (FLC). FLC are the core part in many home and industry automation equipment including air conditioner, microwave oven, washing machine, vehicle navigation, robots etc. It can also be combined with the other area of research such as genetic algorithms and neural network to create hybrid models.

Concept of fuzzy logic is quite old, although it came into limelight after the seminal work of Lofti Zadeh, who is known as the father of fuzzly logic. Zadeh has proposed proper mathematical notation for fuzzy set representation and manipulation.

Imprecision is the inherent property of the data. Fuzzy sets can very effectively models the imprecision in the data. And hence, fuzzy sets are more noise tolerant compared to their counter part – crisp set.

lofti zadeg - father of fuzzy logic
Lofti Zadeh

Fuzzy logic resembles the human thinking and hence it is easy to understand and simple to deal with. It involves the process of fuzzification of variable, fuzzy rule base and fuzzy data base, fuzzy inference model and defuzzification of fuzzy output.

Fuzzification is the process of mapping real valued input to the range 0 to 1. Rule based and data base process the converted fuzzy input into fuzzy output with the help of fuzzy inference system such as Mamdani approach, Takagi-sugeno appraoch etc. And Finally, fuzzified output is converted into crisp value with the help of some defuzzification method.

Find all the necessary resources on the entire course of fuzzy logic here.

Sr.Topic title
1Introduction to Crisp Set: Article | Video
2Operations on Crisp Sets: Article | Video
3What and Why Fuzzy Sets: Article | Video
4Real world Examples of Fuzzy Sets: Article | Video
5Fuzzy Terminologies: Article | Video
6Properties of Crisp Set: Article | Video
7Properties of Fuzzy Set: Article | Video
8Operations on Fuzzy Set: Article | Video
9Crisp Relation – Definition, Operations and Types: Article | Video
10Max-Min Composition of Crisp Relation: Article | Video
11Fuzzy relation – Definition, types and operations: Article | Video
12Fuzzy Composition: Max-min and Max-product: Article | Video
13Distance and Similarity Measure: Article | Video
14 Properties of Relation: Article | Video
15 Fuzzy Membership Function – Complete Guide: Article | Video
16Classical and Fuzzy Logic – Connectives, Tautology & Contradiction: Article | Video
17Logical Proof and Deductive Inference in Classical and Fuzzy Logic: Article | Video
18Defuzzification – What, Why and How? : Article | Video
19Linguistic Variables and Hedges: Article | Video
20Maxima Methods – FoM, LoM and MoM: Article | Video
21Weighted Average Method for Defuzzification: Article | Video
22Center of Gravity (CoG) Method for Defuzzification: Article | Video
23Center of Sums (CoS) Method for Defuzzification: Article | Video
24Center of Largest Area (CoA) Method for Defuzzification: Article | Video
25Defuzzification Methods – Solved Example: Article | Video
26What is Fuzzy Inference System? – Concepts & Foundation: Article | Video
27Mamdani Fuzzy Inference System – Concept: Article | Video
28Mamdani Fuzzy Inference Method – Example: Article | Video
29Fuzzy Control System and Its Applications: Article | Video
30What is Fuzzy Inference System and How it works?: Article | Video
31Designing Fuzzy Controller – Step by Step Guide: Article | Video

 

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