Neural network data analysis for laser-induced thermal acoustics
Abstract
A general, analytical closed-form solution for laser-induced thermal acoustic (LITA) signals using homodyne or heterodyne detection and using electrostrictive and thermal gratings is derived. A one-hidden-layer feed-forward neural network is trained using back-propagation learning and a steepest descent learning rule to extract the speed of sound and flow velocity from a heterodyne LITA signal. The effect of the network size on the performance is demonstrated. The accuracy is determined with a second set of LITA signals that were not used during the training phase. The accuracy is found to be better than that of a conventional frequency decomposition technique while being computationally as efficient. This data analysis method is robust with respect to noise, numerically stable and fast enough for real-time data analysis.
Additional Information
Copyright © Institute of Physics and IOP Publishing Limited 2000. Received 4 February 2000, in final form and accepted for publication 5 April 2000; Print publication: Issue 6 (June 2000) Sam Roweis (Gatsby Computational Neuroscience Unit, University College, London) and Erik Winfree (California Institute of Technology) provided the core source code for the neural network implementation. This work was supported by Advanced Projects Research, Inc and by NASA Langley Research Center under NASA contract NAS1-99016.Files
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Additional details
- Eprint ID
- 1715
- Resolver ID
- CaltechAUTHORS:SCHLmst00
- Created
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2006-02-13Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field
- Caltech groups
- GALCIT