Stable learning mechanism for novel Takagi-Sugeno-Kang type interval-valued fuzzy systems

Yi Han Lee*, Ching Hung Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, we propose a novel Takagi-Sugeno-Kang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propagation (BP) algorithm having an optimal learning rate (adaptive learning rate) to guarantee the stability and faster convergence. Finally, the TIVNFS-A with the optimal BP algorithm is applied in nonlinear system identification to demonstrate the effectiveness and performance.

Original languageEnglish
Title of host publicationIMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Pages1-6
Number of pages6
StatePublished - 2011
EventInternational MultiConference of Engineers and Computer Scientists 2011, IMECS 2011 - Kowloon, Hong Kong
Duration: 16 Mar 201118 Mar 2011

Publication series

NameIMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Volume1

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2011, IMECS 2011
CountryHong Kong
CityKowloon
Period16/03/1118/03/11

Keywords

  • Asymmetric membership function
  • Interval-valued fuzzy system
  • Lyapunov stability theorem
  • Nonlinear system
  • Tsk type

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