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On-Line Identification of Flexible TSK-Type Models

  • Plamen P. Angelov
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 92)

Abstract

In this chapter a novel approach to on-line data-driven identification of flexible rule-based models is considered. It concerns primarily TSK flexible models benefiting from their dual nature: being non-linear, they are quasi-linear and, therefore, convenient for using in on-line identification schemes.

Keywords

Membership Function Fault Detection Linguistic Term Rule Structure Consequent Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Plamen P. Angelov
    • 1
  1. 1.Department of Civil and Building EngineeringLoughborough UniversityLoughboroughUK

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