Linguistic Reasoning Petri Nets for Knowledge Representation and Reasoning

被引：33
|
作者：
Liu, Hu-Chen [1 ,2 ]
You, Jian-Xin [1 ,2 ]
You, Xiao-Yue [2 ]
Su, Qiang [2 ]
机构：
[1] Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China
[2] Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2016年 / 46卷 / 04期
关键词：
Expert systems; fuzzy Petri nets (FPNs); knowledge representation; linguistic; 2-tuples; linguistic production rules; FUZZY PRODUCTION RULES; DECISION-MAKING; AGGREGATION OPERATORS; T-NORM; MODEL; DIAGNOSIS; FRAMEWORK;
D O I：
10.1109/TSMC.2015.2445732
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach.
引用
收藏
页码：499 / 511
页数：13
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