Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2004-04-14
2009-11-10
Vincent, David R (Department: 2129)
Data processing: artificial intelligence
Neural network
Learning task
Reexamination Certificate
active
07617166
ABSTRACT:
A system and method of performing aeroelastic analysis using a neural network. Input parameters, such as mass and location, contributing to aeroelastic characterization are determined and constrained. A model of a structure to be analyzed can be constructed. The model can include a number of locations where the input parameters can be varied. The aeroelastic characteristic of the structure can be analyzed using a finite element model to determine a number of output characteristics, each of which can correspond to at least one of a plurality of input samples. A neural network can be generated for determining the aeroelastic characteristic based on input parameters. The input sample/output characteristic pairs can be used to train the neural network. The weights and bias values from the trained neural network can be used to generate a non-linear transfer function that generates the aeroelastic characteristic in response to input parameters.
REFERENCES:
patent: 4840069 (1989-06-01), Hampton et al.
patent: 5746391 (1998-05-01), Rodgers et al.
patent: 5784739 (1998-07-01), Kawada et al.
patent: 6014024 (2000-01-01), Hockey et al.
patent: 6189830 (2001-02-01), Schnelz et al.
patent: 2003/0191406 (2003-10-01), Eberhart et al.
‘Adaptive nonlinear neural network controller for rotorcraft vibration’, Spencer, Sanner, Chopra, 1997, SPIE vol. 3041, 538-553.
‘Aeroelasticity of morphing wings using neural networks’: Natarajan, 2002, Natarajan, abstract, 21, 44-64, 74.
‘Small Business Innovation Research to Support Aing Aircraft’; 2000; National Academy of Sciences; Publication NMAB-497; http://darwin.nap.edu/books/NI000345/html/47.html; pp. 13-46.
‘Adaptive nonlinear neural network controller for rotorcraft vibration’; 1997, SPIE vol. 3041, 0277-786X, pp. 538-553.
‘Aeroelasticity of Morphing Wings Using Neural Networks’; Anand Natarajan; Jul. 2002; Virginia Polytechnic Institute and State University.
‘Small Business Innovation Research to Support Aging Aircraft’: NMAB-497, 2001, National Academy Press, publication NMAB-497, pp. 11, 21, 29-30, 41-43.
‘Elements of Artificial Neural Networks’: Mehrotra, 1997, MIT Press, pp. 11, 13-14, 65, 103-104, 130-133.
‘Materials mechanics—the basis of advanced technology for ageing aircraft’: Boller, 2001, Werkstofftech, 32, pp. 388-397.
‘Integrated decision support for aviation safety inspectors’: Luxhoj, 1996, Elsevier, Finite elements in analysis and design, pp. 381-403.
Haudrich Darin P.
Pitt Dale M.
Coughlan Peter
Rozenblat IP LLC
The Boeing Company
Vincent David R
LandOfFree
Neural network for aeroelastic analysis does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Neural network for aeroelastic analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Neural network for aeroelastic analysis will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4127025