The objective of this document Is to show the capabilities of parallel hardware-based on-line learning neural networks (NNs). This specific application is related to an on-line estimation problem for sensor validation purposes. Neural-network-based microprocessors are starting to be commercially available. However, most of them feature a learning performed with the classic back-propagation algorithm (BPA). To overcome this lack of flexibility a customized motherboard with transputers was implemented for this investigation, The extended BPA (EBPA), a modified and more effective BPA, was used for the on-line learning, These parallel hardware-based neural architectures were used to implement a sensor failure detection, identification, and accommodation scheme in the model of a night control system assumed to be without physical redundancy in the sensory capabilities. The results of this study demonstrate the potential for these neural schemes for implementation in actual flight control systems of modern high performance aircraft, taking advantage of the characteristics of the extended back-propagation along with the parallel computation capabilities of NN customized hardware.
Sensor validation using hardware-based on-line learning neural networks
IEEE Transactions on Aerospace and Electronic Systems ; 34 , 2 ; 456-468
1998-04-01
1201634 byte
Article (Journal)
Electronic Resource
English
Sensor Validation Using Hardware-Based On-Line Learning Neural Networks
Online Contents | 1998
|AIAA-2002-4996 On-Line Learning RBF Neural Networks for Sensor Validation
British Library Conference Proceedings | 2002
|Using neural networks for sensor validation
AIAA | 1998
|