@Article{cmc.2020.06343,
AUTHOR = {Junhua Xi, Kouquan Zheng, Jianfeng Ma, Jungang Yang, Zhiyao Liang},
TITLE = {Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation  Algorithm for Information Services},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {605--619},
URL = {http://www.techscience.com/cmc/v63n2/38532},
ISSN = {1546-2226},
ABSTRACT = {Intuitionistic fuzzy Petri net is an important class of Petri nets, which can be 
used to model the knowledge base system based on intuitionistic fuzzy production rules. 
In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems, 
a new Petri net modeling method is proposed by introducing BP (Error Back 
Propagation) algorithm in neural networks. By judging whether the transition is ignited 
by continuous function, the intuitionistic fuzziness of classical BP algorithm is extended 
to the parameter learning and training, which makes Petri network have stronger 
generalization ability and adaptive function, and the reasoning result is more accurate and 
credible, which is useful for information services. Finally, a typical example is given to 
verify the effectiveness and superiority of the parameter optimization method.},
DOI = {10.32604/cmc.2020.06343}
}