الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Most of the classes of Petri nets focus on modeling and analyzing
dynamic behavior of static systems. The dynamic behavior as well as the
static structure of the modeled system is governed by Petri net through the
firing of its transition. Since the state and properties of an intelligent
system are variable frequently (knowledge is updated or modified
frequently), modeling approaches should capable of adjusting the model
parameters according to the system dynamics (changes of the system).
This thesis focuses on introducing a new class of Petri nets, adaptive
fuzzy higher order Petri nets (AFHOPN), for modeling intelligent systems.
It takes into account the changes of the weights, is capable of dynamically
adjusting the parameters and has the learning ability.
The proposed model has systematic procedure for supporting the fuzzy
reasoning via two algorithms: reasoning algorithm for computing fuzzy
beliefs, which requires the algebraic forms of the new state equation of the
proposed model, and the other for updating the weights. We also gave the
stability analysis of the proposed model.
We expand this proposed model to apply for two applications:
intelligent E-learning system and intelligent Computer Numerical Control
(CNC) machines.