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Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace ApplicationsRecent 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.
Document ID
20040084434
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Gupta, Pramod
(QSS Group, Inc. Moffett Field, CA, United States)
Loparo, Kenneth
(Case Western Reserve Univ. OH)
Mackall, Dale
(NASA Dryden Flight Research Center Edwards, CA, United States)
Schumann, Johann
(Research Inst. for Advanced Computer Science Moffett Field, CA, United States)
Soares, Fola
(NASA Dryden Flight Research Center Edwards, CA, United States)
Date Acquired
August 21, 2013
Publication Date
January 1, 2004
Subject Category
Aircraft Stability And Control
Meeting Information
Meeting: International Conference on Computational Intelligence on Modeling, Control and Automation (CIMCA)
Location: Gold Coast
Country: Australia
Start Date: July 12, 2004
End Date: July 14, 2004
Distribution Limits
Public
Copyright
Other

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