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.

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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
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
Collection: Centre for Intelligent Systems Research
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