Fuzzy system identification method for cognitive and decision processes

Research output: Contribution to journalConference article

Abstract

The paper discusses a new fuzzy oriented method for estimating the transfer function of multivariable dynamic systems using creative interpolative fuzzy amplification for poorly informed conflicting data sets. The essential information in fuzzy rule bases - by proper techniques - can be concentrated into smaller ones. Fuzzy rule interpolation method (proposed first by Koczy and Hirota) offers a possibility to obtain conclusion for an observation that does not match any of the rule antecedents, therefore, even sparse rule bases can be allowed. On the basis of the Koczy-Hirota fuzzy interpolation approach we introduce an effective transfer function estimation method using this formula in the regularization of the estimated transfer function for dynamical multivariable systems obtained from noisy, uncertain input/output data.

Original languageEnglish
Pages (from-to)1962-1967
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - Dec 1 1998
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 2 (of 5) - San Diego, CA, USA
Duration: Oct 11 1998Oct 14 1998

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Fuzzy system identification method for cognitive and decision processes'. Together they form a unique fingerprint.

  • Cite this