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Author: Rinu Gour\u00a0<\/a><\/p>\n Generally, it\u2019s a method of reasoning. Although, resembles human reasoning. Also, it has an approach to decision-making in humans. As they involve all intermediate possibilities between digital values YES and NO.<\/p>\n The fuzzy Logic System was invented by Lotfi Zadeh. Also, he observed, unlike other computers, it includes a range of possibilities between YES and NO, in a human decision.<\/p>\n Basically, it can be implemented in systems of various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in\u00a0artificial intelligence<\/a>.<\/p>\n Gene<\/span>rally, we use the fuzzy logic system for practical as well as commercial purposes.<\/p>\n Read more about\u00a0What is Expert System in Artificial Intelligence<\/a><\/p>\n Basically, four parts are shown in the architecture of fuzzy logic system-<\/p>\n a. Fuzzification Module<\/p>\n We use this module to transform the system inputs. As the is a crisp number. Also, helps in splitting the input signal into various five steps.<\/p>\n b. Knowledge Base<\/p>\n In this, we have to store it in IF-THEN rules that were provided by experts.<\/p>\n c. Inference Engine<\/p>\n Generally, it helps in simulating the human reasoning process. That is by making fuzzy inferences on the inputs and IF-THEN rules.<\/p>\n d. Defuzzification Module<\/p>\n In this module, we have to transform the fuzzy set into a crisp value. That set was obtained by an inference engine.<\/p>\n Although, the membership functions always work on the same concept i.e fuzzy sets of variables.<\/p>\n This function allows you to quantify linguistic terms. Also, represent a fuzzy set graphically. Although, MF for a fuzzy set A on the universe of discourse. That X is defined as \u03bcA:X \u2192 [0,1].<\/p>\n In this function, between a value of 0 and 1, each element of X is mapped. We can define it as the degree of membership. Also, it quantifies the degree of membership of the element. That is in X to the fuzzy set A.<\/p>\n We can apply different membership functions to fuzzify a numerical value. Also, we use simple functions as complex. As they do not add more precision in the output.<\/p>\n We can define all membership functions for LP, MP, S, MN, and LN. That is shown as below \u2212<\/p>\n There is some common triangular membership function as compared to other functions. Such as singleton, Gaussian. And trapezoidal.<\/p>\na. What is a Fuzzy Logic System?<\/h1>\n
b. Implementation of Fuzzy Logic System<\/h1>\n
Why Fuzzy Logic?<\/h1>\n
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Fuzzy Logic Systems Architecture<\/h1>\n
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