Architecting autonomic computing systems through Probabilistic Software Stability Model (PSSM)
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Novelty detection for a neural network-based online adaptive system
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
Predicting with confidence – an improved dynamic cell structure
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
An approach to v&v of embedded adaptive systems
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
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The appeal of including adaptive components in complex computational systems, such as flight control, is in their ability to cope with a changing environment. Continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques. In safety-critical applications, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we present a non-conventional V&V approach suitable for online adaptive systems. We applied this approach to an adaptive flight control system that employs neural network learning for online adaptation. Presented methodology consists of a Novelty Detection technique andOnline Stability Monitoring tools.The Novelty Detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunovýs Stability Theory detect unstable learning behavior in neural networks.