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