Abstract
A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.
Highlights
Fuzzy logic was introduced by Lotfi A
The membership function is defined in terms of an ordered pair domain knowledge graph (DKG) = (V,E) where V is a set of vertices defined by (Kx,XA) and E is a set of edges where E = {K1,K2} : (K1,R,K2) ε DK is equal to a set of ordered pairs of those concepts from the domain knowledge that are related
Because of its flexibility and linearity, this study proposes the design of trapezoidal-based membership functions for six fuzzy sets “poor”, “weak”, “average”, “good”, “very good” and “excellent” to enable the first fuzzy control process, the fuzzification process to model a trapezium area that has a universe of discourse ranging from 0 to 1
Summary
Fuzzy logic was introduced by Lotfi A. Zadeh of University California at Berkeley to provides a definitive solution to problems of information that may be construed as uncertain or imprecise [1]. It deals with reasoning that is approximate rather than fixed and exact. The concept of fuzzy logic enables reasoning and making rational decisions in circumstances of imprecision, uncertainty, human subjectivity, incomplete information and deficient computations. The basic element of the fuzzy logic theory is the fuzzy set. A fuzzy set describes a characteristic, thing, fact or state. “novice” is a fuzzy set that describes knowledge level, “young” is a fuzzy set that describes age, “cold” is a fuzzy set that describes a body temperature, “tall” is a fuzzy set that describes height, “loud” is a fuzzy set that describes sound’s intensity, “close” is a fuzzy set that describes the distance between two objects [2]
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