A review of neuro-fuzzy systems based on intelligent control
This is an incremental review paper for researchers in control systems engineering.
This paper reviews neuro-fuzzy systems for intelligent control, comparing fuzzy logic and neural networks and presenting an example of a combined method to address the need for adaptive control in complex dynamic systems.
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.