Fuzzy Logic Systems Architecture

Fuzzy Logic Systems Architecture

The design of Fuzzy Logic has the following features:

The basis of the law

This is a set of If-Then rules and terms used to make decisions. However, modern developments in Fuzzy Logic have reduced the number of rules based on rules, and this set of rules is also called the knowledge base.


This is the step at which crisp numbers are converted into incomprehensible sets. A fun set is a collection of items with similar properties. Depending on the nature of the problem, something maybe a set or not. Crisp sets are based on the concept of binary - Answers Yes or No.

Here, error signals and tangible values are converted into standard sets. In any Fuzzy Logic program, fuzzifier separates input signals into five states:

  1. Great good
  2. Good medium
  3. Small
  4. Medium negitive
  5. Large negitive

The fuzzification process converts light inputs, such as room temperature, picks up sensors and transmits them to the control system for further processing. The Fuzzy Logic control system is based on Fuzzy Logic. Conventional household appliances, such as air-conditioners and washing machines, have uncontrolled control systems inside.

Inference engine

The inference engine determines how much the input values and rules match. Rules apply according to the input values obtained. After that, rules are used to improve regulatory actions. The import engine and knowledge base together are called the controller in the Fuzzy Logic system.


This is a dynamic process of fuzzification. Here, odd values are converted into positive values by marking a map. There will be many defuzzification methods, but the best ones are chosen as inputs. This is a complex process in which methods are used, such as the advanced membership process, the weighted method and the centroid method.