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 Lyapunov’s Stability Theory detect unstable learning behavior in neural networks.