# What is fuzzification and defuzzification

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Search MathWorks. Skip to main content. Fuzzification of the input variables. In this figure, the flow proceeds up from the inputs in the lower left, across each row, and then down the rule outputs in the lower right.

Instead of sharp switching between modes based on breakpoints, logic flows smoothly from regions where one rule or another dominates. A consequent is a fuzzy set represented by a membership function, which weights appropriately the linguistic characteristics that are attributed to it.

MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation. The fuzzy OR operator simply selects the maximum of the two values, 0. Buy options. ENW EndNote.

## Membership Functions, Fuzzification and Defuzzification

Hellendoorn, H. This chapter summaries some methods to develop membership functions, briefly discusses the process of fuzzification making crisp sets into fuzzy sets , and illustrates a few defuzzification reducing fuzzy sets into singleton scalar values methods. Ross, T.

Cite chapter How to cite? The mapping then provides a basis from which decisions can be made, or patterns discerned. The two different pieces of the antecedent service is excellent and food is delicious yielded the fuzzy membership values 0. Select a Web Site Choose a web site to get translated content where available and see local events and offers.

Since decisions are based on testing all the rules in a FIS, the rule outputs must be combined in some manner.

## Fuzzy Logic - Membership Function

Membership functions can be defined as a technique to solve practical problems by experience rather than knowledge. Control , 94 , 3—4. Personalised recommendations. In this method, each membership function is weighted by its maximum membership value. The basic structure of this example is shown in the following diagram: