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Evidential reasoning using extended fuzzy Dempster–Shafer theory for handling various facets of information deficiency
Author(s) -
Aminravan Farzad,
Sadiq Rehan,
Hoorfar Mina,
Rodriguez Manuel J.,
Francisque Alex,
Najjaran Homayoun
Publication year - 2011
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20491
Subject(s) - vagueness , dempster–shafer theory , fuzzy logic , data mining , artificial intelligence , computer science , ambiguity , belief structure , ignorance , fuzzy set operations , fuzzy set , machine learning , mathematics , philosophy , epistemology , programming language
This work investigates the problem of combining deficient evidence for the purpose of quality assessment. The main focus of the work is modeling vagueness, ambiguity, and local nonspecificity in information within a unified approach. We introduce an extended fuzzy Dempster–Shafer scheme based on the simultaneous use of fuzzy interval‐grade and interval‐valued belief degree (IGIB). The latter facilitates modeling of uncertainties in terms of local ignorance associated with expert knowledge, whereas the former allows for handling the lack of information on belief degree assignments. Also, generalized fuzzy sets can be readily transformed into the proposed fuzzy IGIB structure. The reasoning for quality assessment is performed by solving nonlinear optimization problems on fuzzy Dempster–Shafer paradigm for the fuzzy IGIB structure. The application of the proposed inference method is investigated by designing a reasoning scheme for water quality monitoring and validated through the experimental data available for different sampling points in a water distribution network. © 2011 Wiley Periodicals, Inc.