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# Fuzzy Logic System Architecture and Its Components in AI

In this tutorial, we will learn about the fuzzy logic system architecture and its components in Artificial Intelligence. By Monika Sharma Last updated : April 15, 2023

## What is Fuzzy Logic System?

The Fuzzy Logic System is a system which uses Fuzzy logic for reasoning. The Fuzzy Logic is a very efficient method for performing human-like reasoning in conditions with uncertainty.

## Components of a Fuzzy Logic Controller

The following are the components of a fuzzy logic system/controller:

1. Knowledge Base
2. Fuzzification Module
3. Inference Engine
4. Defuzzification Module

Now, let us have a look at each of them one by one:

### 1. Knowledge Base

Every system which works on Artificial Intelligence has a Knowledgebase. The Fuzzy logic system is also an AI-based system, and thus it also has its own knowledge base where all the information and data for the reference by the agent is stored. In the Knowledge Base of Fuzzy Logic system, the rules of the Fuzzy Logic set theory are stored. Their rules are present in the form of an if-else ladder. So, whenever the system tries to solve any problem, this if-else ladder is executed and the system then works on the rule that it gets from the matched condition.

### 2. Fuzzification Module

The fuzzification module performs the conversion of the input information. The information is converted into a form which the system can search for in its Knowledge Base. This is done by splitting the sentences into simpler terms and extracting the main terms out of it which are then sent to the inference engine for further processing.

### 3. Inference Engine

The Inference engine is the main component of the Fuzzy Logic System. If compared with the computer parts, our inference engine is the same as the processor of the computer. All the processing of the information takes place inside it. The task of the inference engine is to draw a valid result by analyzing and concluding all the information that it gets from the fuzzification module. This is again done by referring to the rules and prior information present in the Knowledge Base. The final conclusions made are then sent for further modification to the defuzzification module.

### 4. Defuzzification Module

The Defuzzification Module receives the processed information from the Inference Engine. This information contains the conclusion, but still, it is not in the form in which it was received, i.e. user-understandable form. So, the defuzzification module again converts this information into a form which is well accepted by the user.