S-shaped fuzzy flip-flops

Rita Lovassy, László T. Kóczy

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

The multilayer perceptron is an artificial neural network that learns nonlinear function mappings. Nonlinear functions can be represented by multilayer perceptrons with units that use nonlinear activation functions. The neurons in the multilayer perceptron networks typically employ sigmoidal activation function. The next state of the J-K fuzzy flipflops (F3) using Fodor, Yager and Dombi operators present S-shaped characteristics. An interesting aspect of F3-s might be that they have a certain convergent behavior when one of their inputs (e.g. J) is exited repeatedly. If J is considered the equivalent of the traditional input of a neuron (with an adder unit applied before J), K might play a secondary modifier's role, or can just be set fix. The paper proposes the investigation of such possible F3-networks as new alternative types of neural networks.

Original languageEnglish
Pages383-391
Number of pages9
Publication statusPublished - Dec 1 2007
Event8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007 - Budapest, Hungary
Duration: Nov 15 2007Nov 17 2007

Other

Other8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007
CountryHungary
CityBudapest
Period11/15/0711/17/07

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Keywords

  • ANN
  • Dombi t-norm
  • J-K fuzzy flip-flop
  • Sigmoid shaped function
  • T-conorm
  • Yager

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Lovassy, R., & Kóczy, L. T. (2007). S-shaped fuzzy flip-flops. 383-391. Paper presented at 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2007, Budapest, Hungary.