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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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| Published in: | Software quality journal 2007-09, Vol.15 (3), p.309-326 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c303t-e12e0d3446600e20a438e603b33eb438ef936afcf0fe29c95605394a815644d33 |
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| cites | cdi_FETCH-LOGICAL-c303t-e12e0d3446600e20a438e603b33eb438ef936afcf0fe29c95605394a815644d33 |
| container_end_page | 326 |
| container_issue | 3 |
| container_start_page | 309 |
| container_title | Software quality journal |
| container_volume | 15 |
| creator | Liu, Yan Cukic, Bojan Gururajan, Srikanth |
| description | 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] |
| doi_str_mv | 10.1007/s11219-007-9017-4 |
| format | article |
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| source | ABI/INFORM Global; Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
| 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 |
| title | Validating neural network-based online adaptive systems: a case study |
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