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Автор: Hua Shi, Hu-Chen Liu
Издательство: Springer
Год: 2023
Страниц: 476
Язык: английский
Формат: pdf (true)
Размер: 12.2 MB
With the development of Artificial Intelligence, developing expert systems to simulate human thinking has become a hot research topic nowadays. Expert system is an intellectual programming system that uses the knowledge captured from experts to solve specific problems reaching the level of experts. The crucial issues in developing an expert system are the representation of the obtained expert knowledge, the acquisition of experts’ professional knowledge, and the reasoning process of knowledge rules. So far, many knowledge representation methods have been introduced in the literature. Among them, the fuzzy Petri nets (FPNs) are a promising modelling tool for expert systems and have a couple of attractive advantages. Combining fuzzy sets and Petri nets, the FPNs are a graphical and mathematical model tool for representing imprecise information and supporting fuzzy reasoning in expert systems. An FPN is a marked graphical system containing places and transitions, where graphically circles represent places, bars depict transitions, and directed arcs denote the relationships between places and transitions. The main features of an FPN are that it supports visualized representation of information and provides a unified form to deal with imprecise and uncertain knowledge information. Due to these characteristics, the FPN method has been applied to many industrial fields for knowledge representation and reasoning in expert systems.