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

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 nonlinear 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.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Zeshan Kurd, Tim P. Kelly, Jim Austin
• Keywords: Artificial Neural Network, Fuzzy Self-Organising Map, IDEAL 2004, Safety Critical Applications