Abstract
This paper proposes a framework based on the cross-validation methods for constructing and training radial basis function (RBF) neural networks. The proposed growing RBF (GRBF) neural network begins with initial number of hidden units. In the process of training, the GRBF network adjusts the hidden neurons by eliminating some “small” hidden units and splitting one “large” hidden unit at the same cycle. If the prediction error in the system is not less than the pre-given threshold, the proposed method increases hidden units to re-estimate the parameters in the next process of training, until the stop criterion is satisfied. In practice, the proposed GRBF network are evaluated and tested on two real 3D seismic data sets with very favorable self-adaptive ability and satisfactory results.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Broomhead, D.S., Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks. Complex System 2, 321–355 (1988)
Moody, J., Darken, C.: Fast Learning in Networks of Locally Tuned Processing Units. Neural Computation 1, 281–294 (1989)
Poggio, T., Girosi, F.: Regularization Algorithms for learning that are equivalent to multiplayer networks. Science 247, 978–982 (1990)
Davis, J.: Statistics and data analysis in geology, 2nd edn. Wiley (1986)
Bezdek, C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Russo, M., Patanè, G.: Improving the LBG Algorithm. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1606, pp. 621–630. Springer, Heidelberg (1999)
Runkler, A., Bezdek, C.: Alternating Cluster Estimation: A New Tool for Clustering and Function Approximation. IEEE Transactions on Fuzzy Systems 7(3), 377–393 (1999)
Scheevel, J.R., Payrazyan, K.: Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation. Paper 56734, SPE Reservoir Evaluation & Engineering, 64–72 (2001)
Schultz, P.S., Ronen, S., Hattori, M., Corbett, C.: Seismic Guided Estimation of Log Properties, Part 1: A Data-driven Interpretation Technology. The Leading Edge 13, 305–315 (1994)
Ronen, S., Schultz, P.S., Hattori, M., Corbett, C.: Seismic Guided Rstimation of Log Properties, Part 2: Using Artificial Neural Networks for Nonlinear Attribute Calibration. The Leading Edge 13, 674–678 (1994)
Russell, B., Hampson, D., Schuelke, J., Quirein, J.: Multiattribute Seismic Analysis. The Leading Edge 16, 1439–1443 (1997)
Schuelke, J.S., Quirein, J.A.: Validation: A Technique for Selecting Seismic Attributes and Verifying Results. In: 68th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, pp. 936–939. (1998)
Horikawa, S.I., Furuhashi, T., Uchikawa, Y.: On Fuzzy Modeling using Fuzzy Neural Networks with the Back-propagation Algorithm. IEEE Transactions on Neural Networks 3(5), 801–806 (1992)
Author information
Authors and Affiliations
School of Insurance and Economics, University of International Business and Economics, Beijing, China
Yan Li
School of Banking and Finance, University of International Business and Economics, Beijing, China
Hui Wang
BGP INC., China National Petroleum Corporation, China
Jiwei Jia & Lei Li
- Yan Li
You can also search for this author inPubMed Google Scholar
- Hui Wang
You can also search for this author inPubMed Google Scholar
- Jiwei Jia
You can also search for this author inPubMed Google Scholar
- Lei Li
You can also search for this author inPubMed Google Scholar
Editor information
Editors and Affiliations
Key Laboratory of Machine Perception (MOE), School of lectronics Engineering and Computer Science, Department of Machine Intelligence, Peking University, 100871, Beijing, China
Ying Tan
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China
Yuhui Shi
Automation College, Harbin Engineering University, 150001, Harbin, China
Hongwei Mo
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Wang, H., Jia, J., Li, L. (2013). The Growing Radial Basis Function (RBF) Neural Network and Its Applications. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_57
Download citation
Publisher Name:Springer, Berlin, Heidelberg
Print ISBN:978-3-642-38702-9
Online ISBN:978-3-642-38703-6
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative