A Comparative Study between Fuzzy Logic Control and Adaptive Neuro-Fuzzy Control for Water Bath System

Abstract

In this paper a comparison is carried out in order between fuzzy logic controller and adaptive neuro-fuzzy controller. We make use of these two control systems to regulate the temperature of the water bath system. We see that the Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. So the training of these parameters can be carried out by neural networks, which are designed to learn from training data, but which are in general not able to profit from structural knowledge. Thus combining fuzzy controllers and neural networks. The structure of the controller is based on the gradient error back propagation function (BPF) and least square error (LSE), neural network with Gaussian membership functions. We conclude that the proposed adaptive neurofuzzy controller is better than fuzzy controller.

Real time monitoring and intelligent control for novel advanced microwave biodiesel reactor

“…A fuzzy systems can explain the knowledge it encodes but cannot learn or adapt its knowledge from training examples, while neural network can learn from training examples but cannot explain what it has learned [16]. The Neuro-Fuzzy controller is a hybrid neural network and Fuzzy logic controller so it takes advantages of both Fuzzy and neural without the disadvantages [17].…”

The Design of Neuro-Fuzzy Control System Based on Data Fusion

“…With the researching development of the intelligent control, the neural network technique has been combined of fuzzy logic widely through simulating the human's thinking. The combination is used to design the control strategy to solve the problem of complex systems to achieve better control effect [1][2][3]. When the controlled object is more and more complicated, the system is a complex process and has the characteristics of nonlinear and time-varying uncertainty.…”