Abstract
The problem of increasing the accuracy of geonavigation data being used for the control of the drilling oil-gas well trajectory is considered. The approach to solving the problem based on the distortion and measurement noise filtration with the use of the smoothing neural network is proposed. The generalized algorithm of the smoothing neural network design on the basis of the multilevel regularization is discussed. The peculiarities of the algorithm realization with the use of the offered vector regularization criterion of network parameters ranking is considered. The example of smoothing the geonavigation data on the basis of designed RBF network is considered.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tikhonov, A.N., Arsenin, V.A.: Solution of Ill-posed Problems. Winston & Sons, Washington (1977)
Haykin, S.: Neural Networks. A comprehensive foundation. Prentice Hall, New York (1994)
Xu, L.: Data Smoothing regularization, multi-sets-learning, and problem solving strategies. Neural Networks 16(5-6), 817–825 (2003)
Wahba, G.: Spline Models For Observation Data. SIAM, Philadelphia (1990)
Chris, M., Bishop: Training with noise is equivalent to Tikhonov regularization. Neural Computation 7(1), 108–116 (1995)
Burger, M., Neubauer, A.: Analysis of Tikhonov regularization for function approximation by neural networks. Neural Networks 16(1), 79–90 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vasilyev, V., Nugaev, I. (2009). Neural Network Smoothing of Geonavigation Data on the Basis of Multilevel Regularization Algorithm. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-03040-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
eBook Packages: Computer ScienceComputer Science (R0)