Elsevier

Fuzzy Sets and Systems

Volume 87, Issue 3, 1 May 1997, Pages 277-289
Fuzzy Sets and Systems

A note on the integration of fuzzy systems with neural networks under a TLTT framework

https://doi.org/10.1016/S0165-0114(96)00036-XGet rights and content

Abstract

Recently, there has been a considerable amount of interest and practice in combining fuzzy systems with neural networks. Without aiming at giving a thorough review of this field or presenting technical details, this paper tries to provide a unified conceptual framework under which the two types of the systems can be dealt with in a similar manner. We refer to this framework as the two-level-three-term (TLTT) viewpoint. As demonstrated in the paper, this TLTT framework allows us to analyze, discuss, and compare two paradigms in a clear, easy, systematic manner and more importantly provides an informative guideline of how the two paradigms can be better integrated so as to solve the problems at hand; in particular, those problems encountered in engineering fields such as modeling, prediction, classification, and control.

References (24)

  • R. Hecht-Nielsen

    Neurocomputing

    (1990)
  • B. Kosko

    Fuzzy systems as universal approximators

  • Cited by (1)

    View full text