Abstract
One of the key factors limiting the use of neural networks in many industrial applications has been the difficulty of demonstrating that a trained network will continue to generate reliable outputs once it is in routine use. An important potential source of errors arises from input data which differs significantly from that used to train the network. In this paper we investigate the relation between the degree of novelty of input data and the corresponding reliability of the output data. We provide a quantitative procedure for measuring novelty, and we demonstrate its performance using an application involving the monitoring of oil flow in multi-phase pipelines.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Silverman B W (1986) Density Estimation, Chapman and Hall, New York.
Bishop, C.M. and James, G.D. (1993) Monitoring of Multiphase Flows using Dual-Energy Gamma Densitometry and Neural Networks, Nuclear Instruments and Methods in Physics Research A327 580–593.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag London Limited
About this paper
Cite this paper
Bishop, C.M. (1993). Novelty Detection and Neural Network Validation. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_225
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2063-6_225
Published:
Publisher Name: Springer, London
Print ISBN: 978-3-540-19839-0
Online ISBN: 978-1-4471-2063-6
eBook Packages: Springer Book Archive