Extended neuro-fuzzy models of multilayer perceptrons

Dong Zhang, Xiao Li Bai, Kai Yuan Cai

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

In this paper the famous neural model, the multilayer perceptron, is extended to a new neural model that is called the additive-Takagi-Sugeno-type multilayer perceptron. The present study proves that this new model can also act as a universal approximator, and thus it can be used in many fields, such as system modeling and identification. The concept of f-duality and the fuzzy operator interactive-or are used to prove that the proposed neural model is functionally equal to a kind of fuzzy inference system. Further, this paper presents another new neuro-fuzzy model that is called the sigmoid-adaptive-network-based fuzzy inference system. Simulation studies show that our proposed models both have stronger approximation capability than multilayer perceptrons.

Original languageEnglish (US)
Pages (from-to)221-242
Number of pages22
JournalFuzzy Sets and Systems
Volume142
Issue number2
DOIs
StatePublished - Mar 1 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Logic
  • Artificial Intelligence

Keywords

  • Artificial neural network
  • Functional equality
  • Fuzzy rule-based system
  • Interactive-or
  • Neuro-fuzzy modeling
  • Universal approximation
  • f -duality

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