| タイトル | Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications |
| 著者（英） | Gupta, Pramod; Loparo, Kenneth; Schumann, Johann; Soares, Fola; Mackall, Dale |
| 著者所属（英） | NASA Dryden Flight Research Center; NASA Ames Research Center |
| 発行日 | 2004-01-01 |
| 言語 | eng |
| 内容記述 | Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed. |
| NASA分類 | Aircraft Stability and Control |
| 権利 | Copyright |
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