# Fuzzy Logic Concept

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### Fuzzy Logic concept

Fuzzy Logic (FL) is an approach to variable processing that allows for multiple values to be processed through the same variable. Fuzzy logic attempts to solve problems with an open, imprecise spectrum of data that makes it possible to obtain an array of accurate conclusions. (FL) is designed to solve problems by considering all available information and making the best possible decision given the input.

Fuzzy Logic has emerged as a profitable tool for the controlling of subway systems and complex industrial processes, as well as for household and entertainment electronics, diagnosis systems, and other expert systems. Although (FL) was invented in the United States the rapid growth of this technology has started from Japan and has now again reached the USA and Europe also.
(FL) is still booming in Japan, the number of letters patent applied for increases exponentially. The main part deals with rather simple applications of Fuzzy Control.

## Understanding Fuzzy Logic

Fuzzy logic stems from the mathematical study of fuzzy concepts which also involves fuzzy sets of data. Mathematicians may use a variety of terms when referring to fuzzy concepts and fuzzy analysis. Broadly and comprehensively these terms are classified as fuzzy semantics.

In practice, these constructs all allow for multiple values of the “true” condition. Instead of True being numerically equivalent to 1 and False being equivalent to 0 (or vice versa), the True condition could be any number of values less than one and greater than zero. This creates an opportunity for algorithms to make decisions based on ranges of price data as opposed to one discreet data point.

### What Will You Learn?

• Introduction to Fuzzy Logic
• Cartesian Product & Relations on Crisp Set
• Operations and Composition on Crisp Relations
• Relations and Operations on fuzzy set
• Fuzzy compositions - Max-Min & Max-Product
• Equivalence and Tolerance Relations
• Cosine Amplitude and Max-Min Similarity Methods
• Features of Membership Functions and Defuzzification to Crisp Sets
• Defuzzification to Scalars

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