A review of neuro-fuzzy systems based on intelligent control

TL;DR

This paper reviews the structure of intelligent control, focusing on fuzzy logic and neural networks, and discusses their integration for adaptive, self-organizing control systems in complex dynamic environments.

Contribution

It provides a comprehensive review of neuro-fuzzy systems, comparing fuzzy logic and neural networks, and illustrates their combined application in intelligent control.

Findings

Fuzzy logic and neural networks are key components of intelligent control.

Combining fuzzy logic and neural networks enhances control adaptability.

The review highlights the potential of neuro-fuzzy systems for complex systems.

Abstract

The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.