Timetabling Using Adaptive Fuzzy Petri Nets

A Petri net is an abstract formal model of the behavior of a system and information flow. The properties, concepts, and techniques of Petri net are so as to present it as a simple and strong method for describing and analyzing information flow and systems control. Fuzzy Petri Net (FPN) is an appropriate powerful model to emulate knowledge base systems using fuzzy rules. Yet, FPN model does not own learning capability in fuzzy systems. Recently, artificial neural nets have been used to add this capability to FPN. The parameters of fuzzy rules in FPN can be learned by adding the back propagation algorithms of neural nets to FPN. Petri nets with learning ability can be exploited in defining dynamic knowledge base and extrapolating in expert systems. To do so, mostly a generalization of fuzzy Petri net is suggested. This is known as adaptive fuzzy Petri nets. Adaptive fuzzy Petri nets are appropriate for modeling expert systems like university course timetabling system. In this study, university course timetabling is modeled using adaptive fuzzy Petri nets. MATLAB software is suitable for implementing adaptive fuzzy Petri nets. This software is applied to simulate the results of the study.