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About Fuzzy Inference Systems
This document provides an overview of fuzzy inference systems (FIS) and examples of how they work. It discusses the key components of an FIS including fuzzification, rule bases, inference engines, and defuzzification. Two common types of FIS are presented - Mamdani and Sugeno fuzzy models. Examples are provided to illustrate how each type can be used to determine tip amounts based on food and service quality ratings. The examples show how inputs are fuzzified, rules are defined, inference is performed, and outputs are defuzzified using methods like center of gravity and mean of maximum. Guidelines are also given for developing FIS models for other applications based on identifying relevant variables, choosing a model structure, and refining parameters
Detailed Information
| Author: | ['Nik Asyraf Aiman'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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