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A new online updating framework for constructing monotonicity-preserving fuzzy inference systems

conference contribution
posted on 2013-01-01, 00:00 authored by K Tay, T Jee, L Pang, Chee Peng LimChee Peng Lim
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.



Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)


1 - 7




Hyderabad, India

Place of publication

Piscataway, N.J.

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems