Optimization of training sets for neural-net processing of characteristic patterns from vibrating solids

Arthur J. Decker

doi:10.1117/12.449379

26 November 2001 Optimization of training sets for neural-net processing of characteristic patterns from vibrating solids

Arthur J. Decker

Author Affiliations +

Proceedings Volume 4448, Optical Diagnostics for Fluids, Solids, and Combustion; (2001) https://doi.org/10.1117/12.449379
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

Artificial neural networks have been used for a number of years to process holography-generated characteristic patterns of vibrating structures. This technology depends critically on the selection and the conditioning of the training sets. A scaling operation called folding is discussed for conditioning training sets optimally for training feed-forward neural networks to process characteristic fringe patterns. Folding allows feed-forward nets to be trained easily to detect damage-induced vibration-displacement-distribution changes as small as 10 nanometers. A specific application to aerospace of neural-net processing of characteristic patterns is presented to motivate the conditioning and optimization effort.

Citation Download Citation

Arthur J. Decker "Optimization of training sets for neural-net processing of characteristic patterns from vibrating solids", Proc. SPIE 4448, Optical Diagnostics for Fluids, Solids, and Combustion, (26 November 2001); https://doi.org/10.1117/12.449379

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