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
Validating an Online Adaptive System Using SVDD
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Stability monitoring and analysis of online learning neural networks
Stability monitoring and analysis of online learning neural networks
Lyapunov analysis of neural network stability in an adaptive flight control system
SSS'03 Proceedings of the 6th international conference on Self-stabilizing systems
An approach to v&v of embedded adaptive systems
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
An adaptive wrapper algorithm for file transfer applications to support optimal large file transfers
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
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Biologically inspired soft computing paradigms such as neural networks are popular learning models adopted in online adaptive systems for their ability to cope with the demands of a changing environment. However, continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure the reliable performance of such systems. In this paper, we discuss a dynamic approach to validate the adaptive system component. Our approach consists of two run-time techniques: (1) a statistical learning tool that detects unforeseen data; and (2) a reliability measure of the neural network output after it accommodates the environmental changes. A case study on NASA F-15 flight control system demonstrates that our techniques effectively detect unusual events and provide validation inferences in a real-time manner.