Step 2 − Construct membership functions for them Temperature (t) = Įvery member of this set is a linguistic term and it can cover some portion of overall temperature values. For room temperature, cold, warm, hot, etc., are linguistic terms. Linguistic variables are input and output variables in the form of simple words or sentences. Step 1 − Define linguistic variables and terms Convert output data into non-fuzzy values.Convert crisp data into fuzzy data sets using membership functions.Construct knowledge base of rules (start).Construct membership functions for them.Define linguistic Variables and terms (start).This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value. Let us consider an air conditioning system with 5-level fuzzy logic system. Hence the corresponding output also changes. Here, the input to 5-level fuzzifier varies from -10 volts to +10 volts. The triangular membership function shapes are most common among various other membership function shapes such as trapezoidal, singleton, and Gaussian. Simple membership functions are used as use of complex functions does not add more precision in the output.Īll membership functions for LP, MP, S, MN, and LN are shown as below − There can be multiple membership functions applicable to fuzzify a numerical value. y axis represents the degrees of membership in the interval.
x axis represents the universe of discourse. It quantifies the degree of membership of the element in X to the fuzzy set A. It is called membership value or degree of membership. Here, each element of X is mapped to a value between 0 and 1. A membership function for a fuzzy set A on the universe of discourse X is defined as μ A:X →. Membership functions allow you to quantify linguistic term and represent a fuzzy set graphically. The membership functions work on fuzzy sets of variables. Inference Engine − It simulates the human reasoning process by making fuzzy inference on the inputs and IF-THEN rules.ĭefuzzification Module − It transforms the fuzzy set obtained by the inference engine into a crisp value. Knowledge Base − It stores IF-THEN rules provided by experts. It splits the input signal into five steps such as − Fuzzy logic helps to deal with the uncertainty in engineering.įuzzification Module − It transforms the system inputs, which are crisp numbers, into fuzzy sets. It may not give accurate reasoning, but acceptable reasoning. It can control machines and consumer products. It can be implemented in hardware, software, or a combination of both.įuzzy logic is useful for commercial and practical purposes. It can be implemented in systems with various sizes and capabilities ranging from small micro-controllers to large, networked, workstation-based control systems. The fuzzy logic works on the levels of possibilities of input to achieve the definite output. The inventor of fuzzy logic, Lotfi Zadeh, observed that unlike computers, the human decision making includes a range of possibilities between YES and NO, such as − CERTAINLY YES The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. What is Fuzzy Logic?įuzzy Logic (FL) is a method of reasoning that resembles human reasoning. 
Fuzzy Logic Systems (FLS) produce acceptable but definite output in response to incomplete, ambiguous, distorted, or inaccurate (fuzzy) input.
0 kommentar(er)
