File(s) under permanent embargo
A new online updating framework for constructing monotonicity-preserving fuzzy inference systems
conference contributionposted 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.
EventFuzzy Systems. IEEE International Conference (2013 : Hyderabad, India)
Pagination1 - 7
Place of publicationPiscataway, N.J.
Publication classificationE1 Full written paper - refereed
Copyright notice2013, IEEE
Title of proceedingsFUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems
CategoriesNo categories selected
fuzzy inference systemmonotonicityonline updatingfuzzy rule relabelingoptimization-based similarity reasoningbeliefplausibilityevidential mass beliefevidential massScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringSIMILARITY REASONING SCHEMEASSESSMENT MODELS