Online learning neural architectures and cross-correlation analysis for actuator failure detection and identification
This paper describes a study related to the testing and validation of a neural-network based approach for the problem of actuator failure detection and identification following battle damage to an aircraft control surface. Online learning neural architectures, trained with the Extended Back-Propagat...
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| Veröffentlicht in: | International journal of control 1996-02, Vol.63 (3), p.433-455 |
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| container_end_page | 455 |
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| container_issue | 3 |
| container_start_page | 433 |
| container_title | International journal of control |
| container_volume | 63 |
| creator | NAPOLITANO, MARCELLO R. CASDORPH, VAN NEPPACH, CHARLES NAYLOR, STEVE INNOCENTI, MARIO SILVESTRI, GIOVANNI |
| description | This paper describes a study related to the testing and validation of a neural-network based approach for the problem of actuator failure detection and identification following battle damage to an aircraft control surface. Online learning neural architectures, trained with the Extended Back-Propagation algorithm, have been tested under nonlinear conditions in the presence of sensor noise. In addition, a parametric study has been conducted that addresses the selection of 'near optimal' neural architectures for online state estimation purposes. The Failure Detect-ability/False Alarm Rate ratio problem has also been considered in this study. The testing of the approach is illustrated through typical highly nonlinear dynamic simulations of a high performance aircraft. |
| doi_str_mv | 10.1080/00207179608921851 |
| format | Article |
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Online learning neural architectures, trained with the Extended Back-Propagation algorithm, have been tested under nonlinear conditions in the presence of sensor noise. In addition, a parametric study has been conducted that addresses the selection of 'near optimal' neural architectures for online state estimation purposes. The Failure Detect-ability/False Alarm Rate ratio problem has also been considered in this study. The testing of the approach is illustrated through typical highly nonlinear dynamic simulations of a high performance aircraft.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. 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| ispartof | International journal of control, 1996-02, Vol.63 (3), p.433-455 |
| issn | 0020-7179 1366-5820 |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_00207179608921851 |
| source | Taylor & Francis:Master (3349 titles) |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Connectionism. Neural networks Exact sciences and technology |
| title | Online learning neural architectures and cross-correlation analysis for actuator failure detection and identification |
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