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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Schumann, J. Yan Liu |
| Copyright Year | 2007 |
| Description | Author affiliation: NASA Ames, Moffett Field (Schumann, J.) |
| Abstract | The appeal of adaptive control to the aerospace domain should be attributed to the neural network 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 present several advanced methods proposed for verification and validation (V&V) of adaptive control systems, including Lyapunov analysis, statistical inference, and comparison to the well-known Kalman filters. We also discuss two monitoring tools for two types of neural networks employed in the NASA F-15 flight control system as adaptive learners: the confidence tool for the outputs of a Sigma-Pi network, and the validity index for the output of a Dynamic Cell Structure (DCS) network. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 9567292 |
| Page Count | 8 |
| File Format | |
| ISBN | 1424405246 |
| ISSN | 1095323X |
| DOI | 10.1109/AERO.2007.352766 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-03-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Programmable control Adaptive control Aerospace control Neural networks Uncertainty Monitoring NASA Aerodynamics Distributed control |
| Content Type | Text |
| Resource Type | Article |
| Subject | Aerospace Engineering Space and Planetary Science |
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