What are the two different ways to combine neural networks and fuzzy logic technique?

Prediction performance of different models

		Prediction models
Yarn type	Statistical parameter	Regression
Ring	Mean absolute error \%	5.36
Maximum error \%	12.10
Rotor	Correlation coefficient	0.933

Why neural networks and fuzzy techniques are used?

Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem.

What are the application of fuzzy logic?

Fuzzy logic has been used in numerous applications such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, knowledge-based systems for multiobjective optimization of power systems.

How Does fuzzy logic differ from neural network?

The main difference between fuzzy logic and neural network is that fuzzy logic is a reasoning method that is similar to human reasoning and decision making, while the neural network is a system that is based on the biological neurons of a human brain to perform computations.

Which fuzzy operators are utilized in fuzzy set theory?

Explanation: In fuzzy set theory, the fuzzy operators are defined on the fuzzy sets. When the fuzzy operators are anonymous, the fuzzy logic utilizes the IF-THEN rules. I. In contrast to conventional computers, neural networks have much higher computational rates.

What is fuzzy logic in AI Geeksforgeeks?

Fuzzy logic comes with mathematical concepts of set theory and the reasoning of that is quite simple. It provides a very efficient solution to complex problems in all fields of life as it resembles human reasoning and decision-making. The algorithms can be described with little data, so little memory is required.