Naslov
Validating neural network-based online adaptive systems: a case study

Autori
Liu, Yan ; Cukic, Bojan ; Gururajan, Srikanth

Izvornik
Software Quality Journal (0963-9314) 15 (2007), 3; 309-326

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Validation; Online adaptive system; Novelty detection; Support vector data description; Validity index

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA