A Short Fuzzy Logic Tutorial

The purpose of this tutorial is to give a brief information about fuzzy logic systems. The tutorial is prepared based on the studies [2] and [1]. For further information on fuzzy logic, the reader is directed to these studies. A fuzzy logic system (FLS) can be defined as the nonlinear mapping of an input data set to a scalar output data [2]. A FLS consists of four main parts: fuzzifier, rules, inference engine, and defuzzifier. These components and the general architecture of a FLS is shown in Figure 1. Figure 1: A Fuzzy Logic System. The process of fuzzy logic is explained in Algorithm 1: Firstly, a crisp set of input data are gathered and converted to a fuzzy set using fuzzy linguistic variables, fuzzy linguistic terms and membership functions. This step is known as fuzzification. Afterwards, an inference is made based on a set of rules. Lastly, the resulting fuzzy output is mapped to a crisp output using the membership functions, in the defuzzification step. In order to exemplify the usage of a FLS, consider an air conditioner system controlled by a FLS (Figure 2). The system adjusts the temperature of the room according to the current temperature of the room and the target value. The fuzzy engine periodically compares the room temperature and the target temperature, and produces a command to heat or cool the room