Fuzzification of attribute information granules and its formal reasoning model. Issue 3 (25th April 2019)

• Record Type: Journal Article
• Authors: Zhou, Ruqi; Feng, Jiali; Chang, Huiyou; Zhou, Yuepeng
• Is Part Of: CAAI transactions on intelligence technology. Volume 2:Issue 3(2017)
• Page Start: 116
• Page End: 125
• Publication Date: 2019-04-25
• Subjects: cognition -- fuzzy set theory -- Petri nets -- granular computing -- fuzzy logic; attribute fuzzification information -- fuzzy Petri net -- attribute information granule -- formal reasoning model -- attribute theory -- granulation form -- qualitative mapping operator -- formalisation structure -- human cognitive activities -- granular logic model -- knowledge granularity -- knowledge uncertainty -- machine learning model; C1160 Combinatorial mathematics -- C4210 Formal logic; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2017.0014
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : The model of attribute information granule based on the method of attribute theory and the problem of fuzzification of attribute information granules are discussed. The concept of information granule given by Zadeh can be explained with an attribute and its qualitative mapping operator. Finally, this study also discusses the reasoning of granulation form of attribute fuzzification information which is based on the fuzzy Petri net, with the good formalisation structure of fuzzy Petri net, as well as its asynchronous, concurrency, uncertainty, and other characteristics which is similar to those of human cognitive activities, enabling the fuzzy Petri net to express the basic characteristics of a cognitive system in the form of computing attribute granules. The results of this study can provide a reference for the establishment of the granular logic model in the uncertain problems and a new interpretation framework for revealing the inherent laws of knowledge uncertainty's change with knowledge granularity, and also provides another new possible approach to the establishment of the machine learning model of fuzzy Petri net.