Introduction to Fuzzy Logic

Fuzzy Logic Systems Architecture

Basically, there are four parts in the architecture of the fuzzy logic system-

  1. Rule Base: It contains all the rules and “if-then” conditions offered by experts to control decision-making. The most recent update in fuzzy logic provides a number of methods for the design and tuning. Moreover, the update has significantly reduced the number of sets of rules.
  2. Fuzzification: Fuzzification is the second in this series and it helps to convert inputs. It helps in converting crisp numbers to fuzzy sets. Crisp inputs are measured by sensors and passed into a control system for processing. The module is used to transform inputs of the system and also helps in splitting the input signals into five steps:
    — LP: x is a large positive.
    — MP: x is a medium positive.
    — S: x is small.
    — MN: x is medium negative.
    — LN: x is large negative.
  3. Inference Engine: The third one helps in determining the degree of match between fuzzy inputs and fuzzy rules. Based on that percentage it decides which rule is needed to be implemented. After it, to develop the control actions, applied rules are combined. Generally, the process helps in simulating the human reasoning process and that’s by making fuzzy inference on the inputs and “if-then” rules.
  4. Defuzzification: In this module, the transformation of a fuzzy set into crisp value takes place. There are a number of techniques available to do so, and it’s up to the programmer to select the best from the available ones.
  5. Linguistic Variables: Linguistic variables are basically the input and output variables of the system. The values of these variables are mostly words and sentences from the natural languages and no numeric value. Linguistic variables can be decomposed into a set of linguistic terms.

Example: Consider an air conditioner

In this, temperature (t) is a linguistic variable representing the temperature of a room. Terms such as hot and cold are used as per real-life examples to qualify the temperature. These both are linguistic temperature values.

Membership Functions

Membership functions are used in the second and fourth parts of the fuzzy logic architecture to map the non-fuzzy values to linguistic terms and vice-versa. Basically, to quantify a linguistic term a membership function is used.

Fuzzy logic Controller

The information used by people in their daily life is to make and implement easily. Common thumb rules can be applied to those control conditions according to demand. To combat the unwanted effect of system feedback, gaining knowledge is the only powerful weapon.

Use of Fuzzy Logic?

Generally, fuzzy logic systems can be used for practical and commercial purposes. Also, it can be used in –

  • Consumer products and control machines
  • Dealing with uncertainty in engineering
  • Giving at least acceptable reasoning if not accurate reasoning.
  • Very flexible and easy to implement
  • Helps in mimic of the logic of human thoughts
  • Allows a person to build non-linear function of arbitrary complexity
  • Build with complete guidance of experts
  • In fuzzy logic, interference is a process of propagating elastic constraints
  • Highly suitable method for uncertain reasoning

When should fuzzy logic not be used?

Fuzzy logic is never a one-size-fits-all solution. So, it’s important to know in which conditions one should not use fuzzy logic.

  • If it’s not easy for a person to map input space to an output space.
  • Fuzzy logic can’t be applied in situations involving common sense.
  • If controllers can do the job perfectly without the use of fuzzy logic.

How is Fuzzy Logic different from conventional control methods?

FL integrates a simple, rule-based “IF X AND Y THEN Z”. The approach is to solve control problems digitally rather than mathematically. The module is empirically-based on the operator experience and not on their technical understanding.

Fuzzy Logic Applications

These are some applications of fuzzy logic system.

a. Automotive Systems

  • Automatic Gearboxes
  • Four-Wheel Steering
  • Vehicle environment control

b. Consumer Electronic Goods

  • Hi-Fi Systems
  • Photocopiers
  • Still and Video Cameras
  • Television

c. Domestic Goods

  • Microwave Ovens
  • Refrigerators
  • Toasters
  • Vacuum Cleaners
  • Washing Machines

d. Environment Control

  • Air Conditioners/Dryers/Heaters
  • Humidifiers

Advantages of Fuzzy Logic System

  • The Fuzzy logic system is very easy and understandable.
  • The Fuzzy logic system is capable of providing the most effective solution to complex issues.
  • The system can be modified easily to improve or alter the performance.
  • The system helps in dealing engineering uncertainties.
  • It is widely used for commercial and practical purposes.
  • Fuzzy logic systems can be programmed in a situation when feedback sensors stop working.
  • Economical sensor can be used which will help to keep overall system cost low.
  • Robust setup as no precise inputs required.
  • Fuzzy logic can be programmed in a situation where feedback sensor stops working.

Disadvantages of Fuzzy Logic Systems

  • In fuzzy logic setting, exact rules and membership functions are difficult tasks.
  • Fuzzy logic is not always correct, so the results are based on assumptions and may not be widely accepted.
  • In some cases, fuzzy logic is confused with probability theory and terms.
  • Extensive testing with hardware is required for validation and verification of fuzzy knowledge based systems.
  • Fuzzy logic doesn’t have the capability of machine learning and neural network type pattern recognition.

Future Scope

In the near future, there is a tremendous scope of fuzzy logic-based products. One of the major areas where its use is needed to be investigated is the mobile communication system. Moreover, it also opens an opportunity for designing ASICs comprising integrated circuits for computational modules. In recent times, fuzzy logic has emerged as a powerful system in dealing with complex problems. Moreover, its role in handling uncertainty of complex engineering problems is also important. Generally, engineers and scientists face such complex problems which can’t be solved numerically through traditional mathematical methods, but by implementing fuzzy logic to the same, one can characterize and solve the complex problem.


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