Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions
Fuzzy Petri nets (FPNs) have been applied in many fields as a potential modeling tool for knowledge representation and reasoning. However, there exist many deficiencies in the conventional FPNs when applied in the real world. In this paper, we present a new type of FPN, called picture fuzzy Petri ne...
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doaj-276a65136cb7429ea0f002950775da482020-11-25T00:11:31ZengMDPI AGApplied Sciences2076-34172019-03-019598310.3390/app9050983app9050983Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting OpinionsXue-Guo Xu0Hua Shi1Dong-Hui Xu2Hu-Chen Liu3School of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaCollege of Economics and Management, China Jiliang University, Hangzhou 310018, ChinaFuzzy Petri nets (FPNs) have been applied in many fields as a potential modeling tool for knowledge representation and reasoning. However, there exist many deficiencies in the conventional FPNs when applied in the real world. In this paper, we present a new type of FPN, called picture fuzzy Petri nets (PFPNs), to overcome the shortcomings and improve the effectiveness of the traditional FPNs. First, the proposed PFPN model adopts the picture fuzzy sets (PFSs), characterized by degrees of positive membership, neutral membership, and negative membership, to depict human expert knowledge. As a result, the uncertainty, due to vagueness, imprecision, partial information, etc., can be well-handled in knowledge representation. Second, a similarity degree-based expert weighting method is offered for consensus reaching processes in knowledge acquisition. The proposed PFPN model can manage the conflicts and inconsistencies among expert evaluations in knowledge parameters, thus, making the obtained knowledge rules more accurate. Finally, a realistic example of a gene regulatory network is provided to illustrate the feasibility and practicality of the proposed PFPN model.http://www.mdpi.com/2076-3417/9/5/983fuzzy Petri net (FPN)picture fuzzy set (PFS)knowledge representationconflict opinionexpert system |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xue-Guo Xu Hua Shi Dong-Hui Xu Hu-Chen Liu |
spellingShingle |
Xue-Guo Xu Hua Shi Dong-Hui Xu Hu-Chen Liu Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions Applied Sciences fuzzy Petri net (FPN) picture fuzzy set (PFS) knowledge representation conflict opinion expert system |
author_facet |
Xue-Guo Xu Hua Shi Dong-Hui Xu Hu-Chen Liu |
author_sort |
Xue-Guo Xu |
title |
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions |
title_short |
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions |
title_full |
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions |
title_fullStr |
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions |
title_full_unstemmed |
Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions |
title_sort |
picture fuzzy petri nets for knowledge representation and acquisition in considering conflicting opinions |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-03-01 |
description |
Fuzzy Petri nets (FPNs) have been applied in many fields as a potential modeling tool for knowledge representation and reasoning. However, there exist many deficiencies in the conventional FPNs when applied in the real world. In this paper, we present a new type of FPN, called picture fuzzy Petri nets (PFPNs), to overcome the shortcomings and improve the effectiveness of the traditional FPNs. First, the proposed PFPN model adopts the picture fuzzy sets (PFSs), characterized by degrees of positive membership, neutral membership, and negative membership, to depict human expert knowledge. As a result, the uncertainty, due to vagueness, imprecision, partial information, etc., can be well-handled in knowledge representation. Second, a similarity degree-based expert weighting method is offered for consensus reaching processes in knowledge acquisition. The proposed PFPN model can manage the conflicts and inconsistencies among expert evaluations in knowledge parameters, thus, making the obtained knowledge rules more accurate. Finally, a realistic example of a gene regulatory network is provided to illustrate the feasibility and practicality of the proposed PFPN model. |
topic |
fuzzy Petri net (FPN) picture fuzzy set (PFS) knowledge representation conflict opinion expert system |
url |
http://www.mdpi.com/2076-3417/9/5/983 |
work_keys_str_mv |
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