2003 | OriginalPaper | Buchkapitel
Establishing Safety Criteria for Artificial Neural Networks
verfasst von : Zeshan Kurd, Tim Kelly
Erschienen in: Knowledge-Based Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Artificial neural networks are employed in many areas of industry such as medicine and defence. There are many techniques that aim to improve the performance of neural networks for safety-critical systems. However, there is a complete absence of analytical certification methods for neural network paradigms. Consequently, their role in safety-critical applications, if any, is typically restricted to advisory systems. It is therefore desirable to enable neural networks for highly-dependable roles. This paper defines the safety criteria which if enforced, would contribute to justifying the safety of neural networks. The criteria are a set of safety requirements for the behaviour of neural networks. The paper also highlights the challenge of maintaining performance in terms of adaptability and generalisation whilst providing acceptable safety arguments.