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.
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Kurd, Z., Kelly, T.P., Austin, J. (2004). Exploiting Safety Constraints in Fuzzy Self-organising Maps for Safety Critical Applications. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_39
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DOI: https://doi.org/10.1007/978-3-540-28651-6_39
Publisher Name: Springer, Berlin, Heidelberg
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