FUZZY LOGIC CLASSIFICATION
3.1 Matlab’s Fuzzy Logic Toolbox In the lack of precise mathematical model which will describe behaviour of the system, Fuzzy Logic Toolbox is a good “weapon” to solve the problem: it allows using logic if-then rules to describe the system’s behaviour. This Toolbox is a compilation of functions built on the MATLAB® numeric computing environment and provides tools for creating and editing fuzzy inference systems within the framework of MATLAB.
The toolbox provides three categories of tools:
command line functions,
graphical interactive tools and
simulink blocks and examples.
The Fuzzy Logic Toolbox provides a number of interactive tools that allow accessing many of the functions through a graphical user interface (GUI). Fuzzy Logic Toolbox allows building the two types of system:
Fuzzy Inference System (FIS) and
Adaptive Neuro-Fuzzy Inference System (ANFIS).
3.2 Fuzzy inference system
Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The process of fuzzy inference involves: membership functions, fuzzy logic operators and if-then rules. There are two types of fuzzy inference systems that can be implemented in the Fuzzy Logic Toolbox:
Mamdani-type and
Sugeno-type.
Mamdani's fuzzy inference method is the most commonly seen fuzzy methodology and it expects the output membership functions to be fuzzy sets. After the aggregation process, there is a fuzzy set for each output variable that needs defuzzification. Sugeno-type systems can be used to model any inference system in which the output membership functions are either linear or constant. This fuzzy inference system was introduced in 1985 and also is called Takagi-Sugeno-Kang. Sugeno output membership functions (z, in the following equation) are either linear or constant. A typical rule in a Sugeno fuzzy model has the following form:
If Input 1 = x and Input 2 = y, then Output is z = ax + by + c
For a zero-order Sugeno model, the output level z is a constant (a=b =0).
3.2.1 Membership function
Membership function is the mathematical function which defines the degree of an element's membership in a fuzzy set. The Fuzzy Logic Toolbox includes 11 built-in membership function types. These functions are built from several basic functions:
piecewise linear functions,
the Gaussian distribution function,
the sigmoid curve and
quadratic and cubic polynomial curve.
Two membership functions are built on the Gaussian distribution curve: a simple Gaussian curve and a two-sided composite of two different Gaussian curves (Figure 3.)
This type of membership function will be used later on, according to the results coming from PCI.