The Growing Radial Basis Function (RBF) Neural Network and Its Applications
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.
KeywordsRadial Basis Function (RBF) neural network Parameter learning Cross-validation method Geological characteristics
Unable to display preview. Download preview PDF.
- 4.Davis, J.: Statistics and data analysis in geology, 2nd edn. Wiley (1986)Google Scholar
- 8.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)Google Scholar
- 12.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)Google Scholar