Knowledge refinement using fuzzy compositional neural networks

  • Authors:

  • Vassilis Tzouvaras;Giorgos Stamou;Stefanos Kollias

  • Affiliations:

  • Image, Video and Multimedia Laboratory, Institute for Computer and Communication Systems, Department of electrical and Computer Engineering, National Technical university of Athens, Greece;Image, Video and Multimedia Laboratory, Institute for Computer and Communication Systems, Department of electrical and Computer Engineering, National Technical university of Athens, Greece;Image, Video and Multimedia Laboratory, Institute for Computer and Communication Systems, Department of electrical and Computer Engineering, National Technical university of Athens, Greece

  • Venue:
  • ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Fuzzy relations as representational tools and fuzzy compositional operators as reasoning components, are user in this paper in order to represent knowledge expressed in semantic rules. Furthermore, neural representation and resolution of composite fuzzy relation equations provides knowledge refinement and adaptation to a specific context. A two-layer fuzzy compositional neural network is proposed in this work, with a learning algorithm changing the weights and minimize the error of the small context changes.