Fuzzy Inference System: Overview, Applications, Characteristics, Structure & Advantages

A fuzzy inference system is the key unit of a fuzzy logic system. The typical structure of a fuzzy inference system consists of various functional blocks. It uses new methods to solve everyday problems.

A fuzzy inference system may be a computer paradigm supported by fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A nonlinear mapping that derives its output from fuzzy reasoning and a group of fuzzy if-then rules. The mapping domain and range can be multidimensional spaced fuzzy sets or points.

A fuzzy inference system is a system that uses a fuzzy set theory to map inputs to outputs.

Applications of FIS

A fuzzy inference system is used in different fields, for example, information order, choice examination, master system, time arrangement forecasts, advanced mechanics, and example acknowledgment. It is otherwise called a fuzzy rule-based system, fuzzy model, fuzzy logic controller, fuzzy expert system, and fuzzy associative memory.

It is the vital unit of a fuzzy logic system that deals with decision-making and choosing essential tasks. It utilizes the “IF… . At that point” leads alongside the connectors “AND” “OR” to draw fundamental choice standards.

Characteristics of Fuzzy Inference system

  • The yield from FIS is consistently a fuzzy set irrespective of its input which can be fuzzy or crisp.
  • It is necessary to have a fuzzy output when it is used as a controller.
  • A defuzzification unit would accompany the FIS to convert the fuzzy variable into a crisp variable.

Structure of Fuzzy Inference System

The essential structure of a fuzzy inference system comprises three entities:

  • A rule base containing fuzzy rules
  • A database (or dictionary), containing the participation functions utilized in the fuzzy rules.
  • A reasoning mechanism performing the induction made upon the guidelines and the facts given to infer a reasonable output or conclusion.



What is Defuzzification?

Defuzzification is the extraction of a value representing a fuzzy set.

Defuzzification methods:

  1. Centroid of area
  2. Bisector of area
  3. Mean of max
  4. Smallest of max
  5. Largest of max

It is mandatory to have a crisp output in some instances where we use an interference system as a controller.

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Fuzzy Inference System Inputs and Outputs

  • The fundamental fuzzy inference system can take either fuzzy inputs or crisp inputs, yet the yield it produces is quite often fuzzy sets.
  • Sometimes it is important to have a crisp output, particularly in a situation where a fuzzy inference system is utilized as a controller.
  • Therefore, we need a technique of defuzzification to extricate a crisp value to represent a fuzzy set.

Block diagram for a Fuzzy Inference System with Crisp Output  

Popular Fuzzy Inference Systems (Fuzzy Models)

  1. Mamdani Fuzzy Models
  2. Sugeno Fuzzy Models

The core difference between these fuzzy inference systems is in the consequents of their fuzzy rules, and their distinguishing conglomeration and defuzzification procedures.

1. Ebrahim Mamdani Fuzzy Model

This is the most used fuzzy inference system.

Professor Mamdani fabricated one of the primary fuzzy systems to control a steam motor and kettle mix. He applied fuzzy rules put forth by experienced human operators.

Steps for Computing the Output

Following advances should be followed to compute the output from this FIS

Step 1: Deciding a bunch of fuzzy principles

Step 2: Fuzzifying the inputs with the elements of info participation

Step 3: Amalgamating the fuzzified inputs according to the fuzzy guidelines to discover a standard strength

Step 4: Finding the aftereffect of the standard by summarizing the standard strength with the yield participation work

Step 5: Combining the outcomes to get the yield conveyance

Step 6: Performing defuzzification of the output dispersion

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Two Rules Mamdani with Min and Max Operators

The Mamdani FIS using min and max for T-norms and S-norms, subject to two crisp inputs x and y.

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Two Rules Mamdani FIS with Max and Product Operators

The Mamdani FIS using product and max for T-norms and S-norms, subject to two crisp inputs x and y.

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Mamdani composition of three SISO fuzzy outputs

2. Sugeno Fuzzy Model

This model was proposed by Takagi, Sugeno, and Kang.

For developing a scientific approach to generate fuzzy rules from a given set of input-output data.

The format of this rule is given as:

IF x is A and y is B; Z= f(x,y)

Here, AB is fuzzy sets in antecedents, and z= f(x, y) is a crisp function within the consequent.

The most commonly used zero-order Sugeno fuzzy model applies fuzzy rules within the following form:

IF x is A AND y is B; z is k

Where k is a constant

In this case, the output of every fuzzy rule is constant, and every consequent membership function is represented by singleton spikes.


  • First-order Sugeno fuzzy model: f(x, y) – first-order polynomial
  • Zero-order Sugeno fuzzy model: f – constant

Fuzzy reasoning procedure for a first-order Sugeno Fuzzy Model

The fuzzy inference system under Sugeno Fuzzy method works in the following way-

Step 1: Fuzzifying the inputs- the inputs of the system are made fuzzy.

Step 2: Applying the fuzzy operator- the fuzzy operators must be applied to get the output.

Rule Format

The rule format of Sugeno form-

If 7 = x and 9 = y; output is z = ax+by+c

The Sugeno fuzzy inference system is very similar to the Mamdani method.

Only change a rule consequent: instead of a fuzzy set, used a mathematical function of the input variable.

How to Decide Whether to Apply- Mamdani or Sugeno Fuzzy Inference System?

  • Mamdani technique is broadly acknowledged for capturing expert knowledge and information. It allows us to depict the skill in a more instinctive, more human-like way.

However, Mamdani type fuzzy inference entails a considerable computational burden.

  • On the other hand, the Sugeno method is computationally feasible. It functions effectively with advancement and versatile procedures making it exceptionally alluring in versatile issues, particularly for dynamic nonlinear frameworks.

Fuzzy Inference Systems Advantages

Fuzzy Inference System Advantages
Mamdani ●      Intuitive

●      Well-suited to human inputs

●      More interpretable and rule-based

●      Has widespread acceptance

Sugeno ●      Computationally efficient

●      Functions well with linear techniques, like PID control

●      Functions with optimization and adaptive techniques

●      Guarantees output surface continuity

●      Well-suited to mathematical analysis


A fuzzy inference system makes it easier to mechanise any task. This is why the fuzzy inference system has found successful applications in various fields like robotics, pattern recognition, series prediction, etc.

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Which are the main approaches of fuzzy inference systems?

In a fuzzy inference system, an inference rule is a mapping from a set of premise facts to a conclusion fact. There are several approaches to fuzzy inference system design. For example, one approach is based on a set of rules whose premises are all combinations of the input fuzzy sets, while the conclusion is determined by the output fuzzy set. Another is based on a set of rules whose premises are all combinations of the input fuzzy sets, while the conclusion is determined by the complement (negation) of the output fuzzy set. Yet another approach is based on a set of rules whose premises are the input fuzzy sets, and whose conclusions are the complement of the output fuzzy set.

What is an advantage of Sugeno type method?

The advantage of Sugeno type methods is that the number of states is not limited. On the other hand, the number of states is limited in other methods such as Petri nets. Other advantages are:
1. It is free from local minima.
2. The response function can be extended to class-rating and continuous-rating systems.
3. It can be used for discrete-valued variables.

What is fuzzy logic?

Fuzzy logic is a subfield of mathematical logic and computer science that studies methods for implementing approximate reasoning and for manipulating imprecise knowledge. Fuzzy logic allows the truth values of variables to be uncertain. It is often applied to approximate reasoning where the truth values of variables can be intermediate between the values True and False, or, in some cases, even values like Yes and No. In fuzzy logic, a fuzzy inference is an inference with a fuzzy conclusion. For example, an inference like if it is raining, then it is cloudy is a fuzzy inference since the converse is also true.

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