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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/86487
- Input data analysis by neural network
- Hendtlass, Tim
- The back propagation training algorithm, used to train non-linear feed forward multi-layer artificial neural networks, is capable of estimating the error present in the data presented to a network. While of no use during the training of a network, such information can be useful after training to permit the input data to be itself adjusted to better fit the internal model of a trained neural network. After this has been done, the difference between the modified and original data can be useful. This paper discusses how such data adjusting may be done, demonstrates the results for two simple data sets and suggests some uses that may be made of such differences.
- Publication type
- Journal article
- Journal of Computational and Theoretical Nanoscience, Vol. 7, no. 5 (May 2010), pp. 862-867
- Publication year
- FOR Code(s)
- 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics; 0913 Mechanical Engineering; 1007 Nanotechnology
- Artificial neural networks; Back propagation algorithms; Data analysis
- American Scientific Publishers
- Publisher URL
- Copyright © 2010 American Scientific Publishers. The publisher does not allow institutions to archive either the published version or the accepted manuscript of the paper.
- Peer reviewed