A new online updating framework for constructing monotonicity-preserving fuzzy inference systems Tay, Kai Meng, Jee, Tze Ling, Pang, Lie Meng and Lim, Chee Peng 2013, A new online updating framework for constructing monotonicity-preserving fuzzy inference systems, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-7. Attached Files Name Description MIMEType Size Downloads Title A new online updating framework for constructing monotonicity-preserving fuzzy inference systems Author(s) Tay, Kai Meng Jee, Tze Ling Pang, Lie Meng Lim, Chee Peng orcid.org/0000-0003-4191-9083 Conference name Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India) Conference location Hyderabad, India Conference dates 7-10 Jul. 2013 Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems Editor(s) [Unknown] Publication date 2013 Conference series IEEE International Conference on Fuzzy Systems Start page 1 End page 7 Total pages 7 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) fuzzy inference system monotonicity online updating fuzzy rule relabeling optimization-based similarity reasoning belief plausibility evidential mass belief plausibility evidential mass Summary In this paper, a new online updating framework for constructing monotonicity-preserving Fuzzy Inference Systems (FISs) is proposed. The framework encompasses an optimization-based Similarity Reasoning (SR) scheme and a new monotone fuzzy rule relabeling technique. A complete and monotonically-ordered fuzzy rule base is necessary to maintain the monotonicity property of an FIS model. The proposed framework attempts to allow a monotonicity-preserving FIS model to be constructed when the fuzzy rules are incomplete and not monotonically-ordered. An online feature is introduced to allow the FIS model to be updated from time to time. We further investigate three useful measures, i.e., the belief, plausibility, and evidential mass measures, which are inspired from the Dempster- Shafer theory of evidence, to analyze the proposed framework and to give an insight for the inferred outcomes from the FIS model. Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30057153 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Oct 2013, 10:00:51 EST Wed, 23 Oct 2013, 14:39:08 EST Thu, 31 Oct 2013, 10:32:34 EST Tue, 03 Dec 2013, 15:26:41 EST Wed, 04 Dec 2013, 07:39:22 EST Wed, 04 Dec 2013, 07:40:14 EST Wed, 21 Oct 2015, 14:52:14 EST Sat, 22 Jan 2022, 01:32:01 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 495 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 10:00:51 EST