Comparison of Different Fuzzy Implication Algorithms under Self-Learning

Fuzzy Logic System, Sameh. H. Ghwanmeh

Computer Engineering  Department, Hijjawi Faculty for Engineering Technology,

Yarmouk University 211-63,Irbid, Jordan.

sameh@yu.edu.jo

Abedel Rahman  Al-Zoubidi

Computer Engineering Department

Faculty of Engineering

Mutah University

Irbid-Jordan

zoubidi@mutah.edu.jo

Abstract .   

A Self-Learning Fuzzy Logic system can dynamically learn its knowledge-base and generate fuzzy rules based on a given performance criteria. This ability allows the system to be used in applications where the knowledge to describe the process does not exist or the process dynamics are subject to unknown  change. Most of the published Self-Learning Fuzzy Logic systems are based on the Mini-Operation and Max-Product implication procedures. This study describes the performance of a Self-Learning Fuzzy Logic system under other Fuzzy Implication Approaches, such as the Maxmin-Rule and Boolean Implication. A comparison of the simulation results will be presented.

Keywords:

 Fuzzy set theory, fuzzy logic, fuzzy system,  self-learning    system.