Backward fuzzy rule interpolation with multiple missing values

Shangzhu Jin, Ren Diao, Chai Quek, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

12 Citations (SciVal)

Abstract

Fuzzy rule interpolation offers a useful means for reducing the complexity of fuzzy models, more importantly, it makes inference possible in sparse rule-based systems. Backward fuzzy rule interpolation is a recently proposed technique which extends the potential existing methods, allowing interpolation to be carried out when a certain antecedent of observation is absent. However, only one missing antecedent may be inferred or interpolated using the other given antecedents and the consequent. In this paper, two approaches are proposed in an attempt to perform backward interpolation with multiple missing antecedent values. Both approaches assume a restricted model with multiple inputs and a single output, where every rule has the same number of antecedents. Experimental comparative studies are carried out to demonstrate the efficacy of the proposed work.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Fuzzy Systems
DOIs
Publication statusPublished - 2013
EventFuzzy Systems - Hyderabad, Hyderabad, India
Duration: 07 Jul 201310 Jul 2013
Conference number: 22

Conference

ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2013
Country/TerritoryIndia
CityHyderabad
Period07 Jul 201310 Jul 2013

Fingerprint

Dive into the research topics of 'Backward fuzzy rule interpolation with multiple missing values'. Together they form a unique fingerprint.

Cite this