Generalised Adaptive fuzzy Rule Interpolation

Yang, Longzhi, Chao, Fei and Shen, Qian (2017) Generalised Adaptive fuzzy Rule Interpolation. IEEE Transactions on Fuzzy Systems, 25 (4). pp. 839-853. ISSN 1063-6706

[img]
Preview
Text (Article)
GeneralisedAFRI.pdf - Accepted Version

Download (603kB) | Preview
[img]
Preview
Text (Article)
Yang_Chao_Shen.pdf - Published Version
Available under License Creative Commons Attribution.

Download (651kB) | Preview
Official URL: http://dx.doi.org/10.1109/TFUZZ.2016.2582526

Abstract

As a substantial extension to fuzzy rule interpolation that works based on two neighbouring rules flanking an observation, adaptive fuzzy rule interpolation is able to restore system consistency when contradictory results are reached during interpolation. The approach first identifies the exhaustive sets of candidates, with each candidate consisting of a set of interpolation procedures which may jointly be responsible for the system inconsistency. Then, individual candidates are modified such that all contradictions are removed and thus interpolation consistency is restored. It has been developed on the assumption that contradictions may only be resulted from the underlying interpolation mechanism, and that all the identified candidates are not distinguishable in terms of their likelihood to be the real culprit. However, this assumption may not hold for real world situations. This paper therefore further develops the adaptive method by taking into account observations, rules and interpolation procedures, all as diagnosable and modifiable system components. Also, given the common practice in fuzzy systems that observations and rules are often associated with certainty degrees, the identified candidates are ranked by examining the certainty degrees of its components and their derivatives. From this, the candidate modification is carried out based on such ranking. This work significantly improves the efficacy of the existing adaptive system by exploiting more information during both the diagnosis and modification processes.

Item Type: Article
Uncontrolled Keywords: ATMS, Fuzzy inference, GDE, adaptive fuzzy rule interpolation
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 11 Jul 2016 08:46
Last Modified: 09 Dec 2017 19:17
URI: http://nrl.northumbria.ac.uk/id/eprint/27174

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence