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

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 vali...

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Bibliographic Details
Published in:Software quality journal 2007-09, Vol.15 (3), p.309-326
Main Authors: Liu, Yan, Cukic, Bojan, Gururajan, Srikanth
Format: Article
Language:English
Subjects:
Adaptability
Adaptation
Aerospace engineering
Aerospace industry
Aircraft
Algorithms
Automation
Case studies
Computer engineering
Control systems
Distance learning
Learning
Neural networks
Performance evaluation
Real time
Software
Software quality
Studies
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Summary: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. [PUBLICATION ABSTRACT]
ISSN:0963-9314
1573-1367
DOI:10.1007/s11219-007-9017-4