Artículos relacionados a Neural Extensions to Robust Parameter Design

Neural Extensions to Robust Parameter Design - Tapa blanda

Loeffelholz, Bernard Jacob

 
9781288311583: Neural Extensions to Robust Parameter Design
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ISBN 10: 1288311583 ISBN 13: 9781288311583
Editorial: BiblioScholar, 2012

Sinopsis

Robust parameter design (RPD) is implemented in systems in which a user wants to minimize the variance of a system response caused by uncontrollable factors while obtaining a consistent and reliable system response over time. We propose the use of artificial neural networks to compensate for highly non-linear problems that quadratic regression fails to accurately model. RPD is conducted under the assumption that the relationship between system response and controllable and uncontrollable variables does not change over time. We propose a methodology to find a new set of settings that will be robust to moderate system degradation while remaining robust to noise variables within the system RPD has been well developed on single response problems. Sparse literature exists on dealing with multiple responses in RPD and most methods utilize a subjective weighting scheme. To account for multiple responses, we examine the use of factor analysis on the response data. All the proposed techniques are applied to textbook applications to demonstrate their utility. An Air Force application problem is examined to demonstrate the new technique's potential on a real-world problem that is highly non-linear. The application is a detector developed to detect anomalies within hyper-spectral imagery.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

Robust parameter design (RPD) is implemented in systems in which a user wants to minimize the variance of a system response caused by uncontrollable factors while obtaining a consistent and reliable system response over time. We propose the use of artificial neural networks to compensate for highly non-linear problems that quadratic regression fails to accurately model. RPD is conducted under the assumption that the relationship between system response and controllable and uncontrollable variables does not change over time. We propose a methodology to find a new set of settings that will be robust to moderate system degradation while remaining robust to noise variables within the system RPD has been well developed on single response problems. Sparse literature exists on dealing with multiple responses in RPD and most methods utilize a subjective weighting scheme. To account for multiple responses, we examine the use of factor analysis on the response data. All the proposed techniques are applied to textbook applications to demonstrate their utility. An Air Force application problem is examined to demonstrate the new technique's potential on a real-world problem that is highly non-linear. The application is a detector developed to detect anomalies within hyper-spectral imagery.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
BiblioScholar
Año de publicación
2012
Idioma
Inglés
ISBN 10 
1288311583
ISBN 13 
9781288311583
Encuadernación
Tapa blanda
Número de páginas
174
Contacto del fabricante
no disponible
Persona responsable
gpsr@libri.de
gpsr@libri.de

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