Bipolar fuzzy Petri nets for knowledge representation and acquisition considering non-cooperative behaviors

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning. As a new type of FPNs, bipolar fuzzy Petri nets (BFPNs) are developed in this article to overcome the shortcomings and improve the performance of traditional FPNs. In order to depict expert knowledge mo...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 11; no. 10; pp. 2297 - 2311
Main Authors Xu, Xue-Guo, Xiong, Yun, Xu, Dong-Hui, Liu, Hu-Chen
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Control
Engineering
Expert systems
Fuzzy logic
Fuzzy sets
Knowledge acquisition
Knowledge representation
Mechatronics
Original Article
Pattern Recognition
Petri nets
Reasoning
Robotics
Subject specialists
Systems Biology
Weighting methods
Fuzzy Petri net (FPN)
Knowledge acquisition
Expert system
Bipolar fuzzy set (BFS)
Knowledge representation
Online AccessGet full text
ISSN1868-8071
1868-808X
DOI10.1007/s13042-020-01118-2

Cover