What Is Fuzzy Logic, Working, Benefits, Drawbacks, Applications

Hello guys, welcome back to our blog. In this article, we will discuss what is fuzzy logic, the working of fuzzy logic, its advantages, disadvantages, and applications of it, and we will try to keep this article as simple as possible.

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What Is Fuzzy Logic

Fuzzy logic is used as a technique for making human decisions while using a machine learning platform or Artificial intelligence. In a general way, it can be described as considering the true variable values between 0 and 1. The representation of the real numbers between the binary numbers 0 and 1 is implemented using Fuzzy logic.

To obtain accurate values as solutions for problems fuzzy logic is used. Fuzzy logic considers human reasoning as the key data structure to making accurate decisions. Fuzzy logic grasps the accurate value from the decision tree type of analysis. This logic was developed by Lotfi Zadeh at the University of California in 1965 and named it Fuzzy.

A fuzzy logic controller is a system that is used to control the working of a physical system with the help of fuzzy_logic. FLC is used to control washing machines, air conditioners, heater systems, fan regulators, traffic control, braking system controller, etc. we use FLC where an exact mathematical formulation of the problem is not possible.

These difficulties are due to the time-varying nature of the given logic process. FLC logic is widely used in machine control. Fuzzy logic refers to true or false. The fuzzy controller consists of three stages namely the input stage, processing stage, and output stage.

The input variables are mapped by the sets of membership functions known as fuzzy sets. A control system may have a truth table value equal to either 0 or 1. The controller accepts the input given by the system which is then given and maps them into their membership functions and truth values.

If the range of an error is from -1 to +1, with the analog to digital converter having a resolution of 0.25, then the input variables fuzzy set is described very simply in the form of a table.

Working Of Fuzzy Logic

Fuzzy logic basically works on the sets. There are different types of assumptions in the sets the output will be accurately obtained from the assumed values of sets. The basic configuration used in the fuzzy logic is if-else sets. The accurate answer will be obtained from the input variations, the if-else considerations will give us the accurate value as output.

The inputs are classified into different memberships to obtain the outputs. The inputs will be assigned the function of membership using if-else logic. In the fuzzy logic in a multi-input system, there are different types of input variables so the output of the system will be given as the AND operation of the given input variables.

This logic is used as a standard controller in some platforms, as it consists of different types of parts such as a Fuzzifier, controller, and Defuzzifier. The control of the temperature of the Automatic cooling system will depend on the room temperature, so here different room temperature is considered as input and the output is obtained using the fuzzy_logic which produces the controlled output. This is the basic working of fuzzy logic.

The working principle of the fuzzy logic controller is the output depends on the probability of the state of the input. It works on the concept of the decision of the output which is based on assumptions. It works based on fuzzy sets. Each set consists of linguistic variables defining the state of the output.

Suppose that A is a subset of X and all the members of A lie in the interval 0 and 1. Then set A is known as a fuzzy set. The output depends upon the degree of membership of X in the set. The fuzzy sets are represented graphically with the help of the membership function. The output depends on the logic of AND operation of the state of the input and also the rate of change of the input.

The fuzzy logic controller performs the fuzzy_logic operation in which assigning the outputs is based on the information of linguistic variables. This controller consists of membership functions and fuzzy rules which are obtained by the knowledge of the system operation according to the environment.

The temperature sensor measures the temperature value. The obtained values are then given to the fuzzifier. This fuzzifier assigns linguistic variables for the individual measured value and the rate of change of measured value.

The function of the knowledge base contains the information regarding these member functions as well as the rule base. The next step is to convert the linguistic output variables to numerical variables which are used to drive the motor.

Applications Of Fuzzy Logic

These systems are suitable for the process with high complexity:

Air conditioners
Robotics
Autonomous vehicles
Controlling and optimization of industrial processes and systems.
Fuel efficiency in engines.
Refrigerator, washing machine.
Auto-pilot is based on Fuzzy_logic. This makes the applications in the transportation system. The underwater autonomous vehicles are worked on the basis of fuzzy logic.
It is also applicable in Defence such as locating the targets, Generating infrared images for target recognition. Mostly supports the naval defense systems.
The water purification and the quality checking are done using fuzzy_logic. Structural data handling is also done by using this logic in Industrial sectors.
In the medical sector, it is mostly used in the diagnosis of cancer and diabetic patients.
In many automation sectors, fuzzy logic is used for simplification purposes.

Advantages Of Fuzzy Logic

This system is flexible and can also allow modifications.
The fuzzy_logic systems can be easily constructed.
These systems provide solutions to complex solutions.
The logic is robust, and simple, and can be modified according to our requirements.
This logic can handle different types of inputs at a time and can make an accurate decision using précised functions.
This logic systems have a simple structure and can be constructed easily
The space requirement is less for this type of logic and they can be coded using fewer data.
The decisions can be taken easily as it resembles human reasoning in its logic system, which makes it solve the more complex problems with ease.
If the feedback system fails in the logic system then it can be easily reprogrammable.

Disadvantages Of Fuzzy Logic

Regular upgrades in the fuzzy logic control system are required
These systems will not able to recognize machine learning and neural network platforms
The basic drawback of fuzzy logic is it is completely dependent on human intelligence and expertise.
These are not widely used due to the acquisition of inaccurate data.
The efficiency of the system is not high because they majorly work on inaccurate inputs.
A fuzzy logic controller totally depends on human knowledge and expertise. These controllers cannot recognize machine learning or neural networks.

This was about what is fuzzy_logic, the working of fuzzy_logic, advantages, disadvantages, and applications of fuzzy logic. I hope this article may help you all a lot. Thank you for reading.

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Chetan Shidling

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