Fuzzy Logic Research Paper

FUZZY LOGIC
In recent years, the number and variety of applications of fuzzy logic have increased significantly. The applications range from consumer products such as cameras, camcorders, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and portfolio selection.
To understand why use of fuzzy logic has grown, you must first understand what is meant by fuzzy logic.
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalve logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership

Why should that be? As Lotfi Zadeh, who is considered to be the father of fuzzy logic, once remarked: "In almost every case you can build the same product without fuzzy logic, but fuzzy is faster and cheaper.".
WHEN NOT TO USE FUZZY LOGIC?
Fuzzy logic is not a cure-all. When should you not use fuzzy logic? The safest statement is the first one made in this introduction: fuzzy logic is a convenient way to map an input space to an output space. If you find it 's not convenient, try something else. If a simpler solution already exists, use it. Fuzzy logic is the codification of common sense — use common sense when you implement it and you will probably make the right decision. Many controllers, for example, do a fine job without using fuzzy logic.
However, if you take the time to become familiar with fuzzy logic, you 'll see it can be a very powerful tool for dealing quickly and efficiently with imprecision and nonlinearity.
WHAT CAN FUZZY LOGIC TOOLBOX SOFTWARE

If the service is poor or the food is rancid, then tip is cheap.
2. If the service is good, then tip is average.
3. If the service is excellent or the food is delicious, then tip is generous.
We 'll assume that an average tip is 15%, a generous tip is 25%, and a cheap tip is 5%. It 's also useful to have a vague idea of what the tipping function should look like. A simple tipping function is shown as in Fig.2. Obviously the numbers and the shape of the curve are subject to local traditions, cultural bias, and so on, but the three rules are pretty universal. Now we know the rules, and we have an idea of what the output should look like. Let 's begin working with the GUI tools to construct a fuzzy inference system for this decision process. fig The Tipping Function
THE FIS EDITOR:
The following discussion walks you through building a new fuzzy inference system from scratch. If you want to save time and follow along quickly, you can load the already built system by typing fuzzy tipper This will load the FIS associated with the file tipper.fis (the .fis is implied) and launch the FIS Editor. However, if you load the pre-built system, you will not be building rules and constructing membership