Topology representing networks
Neural Networks
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Toward V&V of neural network based controllers
WOSS '02 Proceedings of the first workshop on Self-healing systems
Adaptive Control Software: Can We Guarantee Safety?
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Lyapunov analysis of neural network stability in an adaptive flight control system
SSS'03 Proceedings of the 6th international conference on Self-stabilizing systems
Validating neural network-based online adaptive systems: a case study
Software Quality Control
Predicting with confidence – an improved dynamic cell structure
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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Rigorous Verification and Validation (V& V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V& V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online 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 Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology.