Recurrent neuro-fuzzy systems

Can Isik, Mohammad Farrokhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

In this paper we introduce a new architecture called recurrent neuro-fuzzy (RNF) system which enhances the modeling capabilities of fuzzy systems with the dynamic behavior of recurrent neural networks (RNN). In a general sense, the architecture of RNF is similar to other adaptive neuro-fuzzy systems. It has a rule-base, a database, an inference engine, and a learning mechanism. In this paper we will emphasize those portions which are different that other approaches, specifically, the construction and operation of recurrent rules and the learning mechanism which is used in determination and adaptation of system parameters. The fundamental concepts of the RNF system are demonstrated using a two-link robot example.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
PublisherIEEE Computer Society
Pages362-366
Number of pages5
StatePublished - 1997
EventProceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97 - Syracuse, NY, USA
Duration: Sep 21 1997Sep 24 1997

Other

OtherProceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97
CitySyracuse, NY, USA
Period9/21/979/24/97

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

Fingerprint Dive into the research topics of 'Recurrent neuro-fuzzy systems'. Together they form a unique fingerprint.

  • Cite this

    Isik, C., & Farrokhi, M. (1997). Recurrent neuro-fuzzy systems. In Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS (pp. 362-366). IEEE Computer Society.