Dynamic TSK systems supported by fuzzy rule interpolation: An initial investigation

Pu Zhang, Qiang Shen

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

1 Citation (SciVal)
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Abstract

Takagi-Sugeno-Kang (TSK) Systems form one type of conventional fuzzy rule inference system, providing an effective approach for performing prediction and regression tasks. In a real-world application, the inputs are usually varying against time, thereby requiring dynamically maintaining the rule base in order to maintain and possibly improve the efficacy of such a system. Situations may become more complicated if the training data does not sufficiently cover the problem space. Fuzzy Rule Interpolation (FRI) systems may help, whilst most of which follow a static approach, tending to process a large amount of interpolated rules which are generally discarded once the results are derived. Yet, the interpolated rules may contain potentially useful information. This paper presents a dynamic TSK system by exploiting such rules to support subsequent inference and promote rule bases. The obtained intermediate rules are directly added into the sparse rule base until it reaches a certain size. Afterwards, a clustering algorithm is employed to categorise rules into different groups so that an interpolated conclusion can be computed using the closest rules selected from a small number of closest rule clusters. Through systematic experimental comparisons with the conventional static approach, it is demonstrated that the proposed dynamic TSK system not only improves the overall reasoning accuracy but also reduces the interpolation overheads by avoiding the need for interpolations of experienced similar observations.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Fuzzy Systems
Subtitle of host publicationFUZZ-IEEE
PublisherIEEE Press
ISBN (Electronic)9781728169323, 9781728169330
DOIs
Publication statusPublished - 26 Aug 2020
EventFuzzy Systems - Glasgow, United Kingdom of Great Britain and Northern Ireland
Duration: 19 Jul 202024 Jul 2020
Conference number: 29

Publication series

NameIEEE International Conference on Fuzzy Systems
PublisherIEEE
Volume2020
ISSN (Print)1098-7584
ISSN (Electronic)1558-4739

Conference

ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2020
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
CityGlasgow
Period19 Jul 202024 Jul 2020

Keywords

  • Closest rule clusters
  • Dynamic fuzzy interpolation
  • Fuzzy rule interpolation
  • Rule clustering
  • TSK systems

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