
In this Tutorial we will learn the motivation behind fuzzy logic. Why it works in solving real world problems which require precision.
In this tutorial we will learn what is fuzzy set and how to represent it in terms of membership grades.
In this tutorial we will explore different types of membership functions and how to visualize them in Matlab
In this tutorial we will learn about fuzzy rules base and if-else implications.
In this tutorial we will learn how to combine more than one fuzzy rule to get a fuzzy output
In this tutorial we will understand the process of defuzzification of fuzzy output graph.
All traditional logic habitually assumes that precise symbols are being employed. It is therefore not applicable to this terrestrial life but only to an imagined celestial existence. [Bertrand Russell, 1923 British philosopher, and Nobel Laureate].
The quotes above, all of them legendary, have a common thread. That thread represents the relationship between precision and uncertainty. The more uncertainty in a problem, the less precise we can be in our understanding of that problem. It is ironic that the oldest quote, above, is due to the philosopher who is credited with the establishment of Western logic – a binary logic that only admits the opposites of true and false, a logic which does not admit degrees of truth in between these two extremes. In other words, Aristotelian logic does not admit imprecision in truth. However, Aristotle’s quote is so appropriate today; it is a quote that admits uncertainty.
This course is specially designed for beginners in Soft computing - Fuzzy logic. It will cover the basics of fuzzy set theory and presents different problems where one can apply this concept. In this course, you will learn how to implement fuzzy logic for problems involving uncertainties and vagueness. This course will act as a foundation course for the researchers working in different areas of science and engineering.