Artificial Intelligence Fuzzy Logic Systems

Introduction to Fuzzy Logic in AI

What is Fuzzy Logic?

Generally, it’s 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.

Fuzzy Logic 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.

Fuzzy Logic Implementation

Basically, it can be implemented in systems with 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 artificial intelligence.

Why Fuzzy Logic?

Generally, we use it for the practical as well as commercial purposes.

• We can use it to consumer products and control machines.
• Although, not give accurate reasoning, but acceptable reasoning.
• Also, this logic helps to deal with the uncertainty in engineering.

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Fuzzy Logic Systems Architecture

Basically, four parts are shown in this-

a. Fuzzification Module

We use this module to transform the system inputs. As this is crisp number. Also, helps in splitting the input signal into various five steps.

LP

x is Large Positive

MP

x is Medium Positive

S

x is Small

MN

x is Medium Negative

LN

x is Large Negative

b. Knowledge Base

In this, we have to store it in IF-THEN rules that was provided by experts.

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c. Inference Engine

Generally, it helps in simulating the human reasoning process. That is by making fuzzy inference on the inputs and IF-THEN rules.

d. Defuzzification Module

In this module, we have to transform fuzzy set into a crisp value. That set was obtained by an inference engine.

Although, the membership functions always work on a same concept i.e fuzzy sets of variables.

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6. Membership Function

As this function allows you to quantify linguistic term. Also, represent a fuzzy set graphically. Although, MF for a fuzzy set A on the universe of discourse. That X is defined as μA:X → [0,1].

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.

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x-axis– It represents the universe of discourse.

y-axis – It represents the degrees of membership in the [0, 1] interval.

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.

We can define all membership functions for LP, MP, S, MN, and LN.

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