Design of Neuro-Fuzzy Controller for Water-Level Tank utilizing Genetic Algorithms

Authors

• Mohamed Z. ALFaiz, Head of Computer Eng.Dept. College of Eng./AL-Nahrain University
• Sawsan Kamal, Computer Science Dept. College of Science /AL-Nahrain University

Document Type

Original Study

Abstract

Fusion of Artificial Neural networks (ANNs) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing needs of adaptive intelligent systems to solve real world problems. In this paper, Fuzzy Logic (FL), Neural Network (NN), and Genetic Algorithms (GAs) were combined to design and tune a neuro-fuzzy controller (NFC). This design is based on multi-layer neuro-fuzzy network. The adaptation of neuro-fuzzy rules consequents and tuning of NFC weights is accomplished utilizing genetic algorithms. Then ineffective rules are removed from the rule-base of the controller. A real code representation is used to encode the GA chromosome. Two selection methods are used, namely, Roulette wheel and Tournament selection methods. The steps of building, tuning, and the removal of the ineffective rules are accomplished in an off-line phase. In on-line phase, the resulting NFC is operated and it is noticed that the response of the system is not robust

How to Cite This Article

ALFaiz, Mohamed Z. and Kamal, Sawsan (2005) "Design of Neuro-Fuzzy Controller for Water-Level Tank utilizing Genetic Algorithms," Iraqi Journal of Computers, Communications, Control and Systems Engineering: Vol. 5: Iss. 2, Article 4.
Available at: https://ijccce.researchcommons.org/journal/vol5/iss2/4