The appeal of including biologically inspired soft computing systems such as neural networks in complex computational systems is in their ability to cope with a changing environment. Unfortunately, continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques to assure the reliable performance of such systems. At the system input layer, novel data may cause unstable learning behavior which may contribute to system failures. Thus, the changes at the input layermust be observed, diagnosed, accommodated and well understood prior to system deployment. Moreover, at the system output layer, the uncertainties/novelties existing in the neural network predictions also need to be well analyzed and detected during system operation.

Our research tackles the novelty detection problem at both layers using two different methods. We use a statistical learning tool, Support Vector Data Description (SVDD), as a one-class classifier to examine the data entering the adaptive component and detect unforeseen patterns that may cause abrupt system functionality changes. At the output layer, we define a reliability-like measure, the validity index. The validity index reflects the degree of novelty associated with each output and thus can be used to perform system validity checks. Simulations demonstrate that both techniques effectively detect unusual events and provide validation inferences in a near-real time manner.