48
B. Uzun et al.
maker define the problem based on the fuzzy logic by fuzzification in order to give
the ranking to the alternative.
Keywords Fuzzy logic · Fuzzy operations · Fuzzy based multi criteria decision
making
8.1 Introduction
The Fuzzy logic comprises of a mathematical and logical variables that distinguish
logical values, indicating a complete, partial and false truth, ranging from numbers
0 to 1 (Zadeh 1973). In other words, Fuzzy logic tends to analyze natural language
data mathematically. The technique characterizes transitional values, which vary
from real and false when evaluated.
Fuzzy logic predates to its evolution in the 1920’s when it was studied and evaluated by Tarski and Lukasiewicz and later introduced and proposed by Lotfi Zadeh
in 1965. Fuzzy method was introduced as a result of uncertainty in evaluation and
analysis (Zadeh 1968). This process is able to give a precise and elaborate analysis.
The fuzzy logic, gives a proper assessment of any introduced variable and take values
from 0 to 1 range which opposes the binary logic (Uzun and Kiral 2017). It is structured into three stages: fuzzification, defuzzification and rules inferences. The fuzzy
logic control (FLC) structure introduced by Zadeh, proposes procedure that assists
in calculating and evaluating variables that are referred to “rules induction”.
Strengths Of Fuzzy Logic (Ibarra and Webb 2016):
• The FLC can be useful for two main purposes, the commercial and practical.
• The FLC controls problems affiliated to input variations and function
• The system is not limited to a particular control outputs or inputs.
• FLC manages non-linear systems which cannot or won’t be able to be managed
in a mathematically approach.
• Fuzzy logic is designed to control and override other target control processes.
• FLC assists in determining engineering uncertainty
Weaknesses Of Fuzzy Logic (Ibarra and Webb 2016):
• The FLC has no ability of machine learning recognition.
• It is observed, there’s a huge contradiction in recognizing fuzzy logic from
probability theory.
• Fuzzy logic has no accuracy
• The system lacks the potential technique used for comparison
• An accurate testing and hardware is needed for the verification of the device or
model.
8.2 Fuzzy Logic Sets
Fuzzy logic sets is the extension of the classical set. These sets are objects collected
with a desired or shared characters. In other words, Classical sets and Fuzzy sets are
B. Uzun et al.
maker define the problem based on the fuzzy logic by fuzzification in order to give
the ranking to the alternative.
Keywords Fuzzy logic · Fuzzy operations · Fuzzy based multi criteria decision
making
8.1 Introduction
The Fuzzy logic comprises of a mathematical and logical variables that distinguish
logical values, indicating a complete, partial and false truth, ranging from numbers
0 to 1 (Zadeh 1973). In other words, Fuzzy logic tends to analyze natural language
data mathematically. The technique characterizes transitional values, which vary
from real and false when evaluated.
Fuzzy logic predates to its evolution in the 1920’s when it was studied and evaluated by Tarski and Lukasiewicz and later introduced and proposed by Lotfi Zadeh
in 1965. Fuzzy method was introduced as a result of uncertainty in evaluation and
analysis (Zadeh 1968). This process is able to give a precise and elaborate analysis.
The fuzzy logic, gives a proper assessment of any introduced variable and take values
from 0 to 1 range which opposes the binary logic (Uzun and Kiral 2017). It is structured into three stages: fuzzification, defuzzification and rules inferences. The fuzzy
logic control (FLC) structure introduced by Zadeh, proposes procedure that assists
in calculating and evaluating variables that are referred to “rules induction”.
Strengths Of Fuzzy Logic (Ibarra and Webb 2016):
• The FLC can be useful for two main purposes, the commercial and practical.
• The FLC controls problems affiliated to input variations and function
• The system is not limited to a particular control outputs or inputs.
• FLC manages non-linear systems which cannot or won’t be able to be managed
in a mathematically approach.
• Fuzzy logic is designed to control and override other target control processes.
• FLC assists in determining engineering uncertainty
Weaknesses Of Fuzzy Logic (Ibarra and Webb 2016):
• The FLC has no ability of machine learning recognition.
• It is observed, there’s a huge contradiction in recognizing fuzzy logic from
probability theory.
• Fuzzy logic has no accuracy
• The system lacks the potential technique used for comparison
• An accurate testing and hardware is needed for the verification of the device or
model.
8.2 Fuzzy Logic Sets
Fuzzy logic sets is the extension of the classical set. These sets are objects collected
with a desired or shared characters. In other words, Classical sets and Fuzzy sets are