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Exploiting Safety Constraints in Fuzzy Self-organising Maps for Safety Critical Applications

  • Zeshan Kurd
  • Tim P. Kelly
  • Jim Austin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

This paper defines a constrained Artificial Neural Network (ANN) that can be employed for highly-dependable roles in safety critical applications. The derived model is based upon the Fuzzy Self-Organising Map (FSOM) and enables behaviour to be described qualitatively and quantitatively. By harnessing these desirable features, behaviour is bounded through incorporation of safety constraints – derived from safety requirements and hazard analysis. The constrained FSOM has been termed a ’Safety Critical Artificial Neural Network’ (SCANN) and preserves valuable performance characteristics for non-linear function approximation problems. The SCANN enables construction of compelling (product-based) safety arguments for mitigation and control of identified failure modes. Illustrations of potential benefits for real-world applications are also presented.

Keywords

Artificial Neural Network Failure Mode Artificial Neural Network Model Fuzzy Rule Firing Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Zeshan Kurd
    • 1
  • Tim P. Kelly
    • 1
  • Jim Austin
    • 2
  1. 1.High Integrity Systems Engineering Group 
  2. 2.Advanced Computer Architectures Group, Department of Computer ScienceUniversity of YorkYorkUK

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