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Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

Junhua Xi1, *, Kouquan Zheng1, Jianfeng Ma1, Jungang Yang1, Zhiyao Liang2
1 College of Information and Communication, National University of Defense Technology, Xi’an, 710106, China.
2 Macau University of Science and Technology, Taipa, Macau.
* Corresponding Author: Junhua Xi. Email: .

Computers, Materials & Continua 2020, 63(2), 605-619. https://doi.org/10.32604/cmc.2020.06343

Received 10 February 2019; Accepted 26 July 2019; Issue published 01 May 2020

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.

Keywords

Intuitionistic fuzzy set, intuitionistic fuzzy Petri nets, production rule, BP algorithm.

Cite This Article

J. Xi, K. Zheng, J. Ma, J. Yang and Z. Liang, "Intuitionistic fuzzy petri nets model based on back propagation algorithm for information services," Computers, Materials & Continua, vol. 63, no.2, pp. 605–619, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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