The Growing Radial Basis Function(RBF)Neural Network and Its Applications

摘要：

　　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.

关键词： Radial Basis Function (RBF) neural network Parameter learning Cross-validation method Geological characteristics

作者: Yan Li Hui Wang Jiwei Jia Lei Li

作者单位: School of Insurance and Economics, University of International Business and Economics, Beijing, Chin School of Banking and Finance,University of International Business and Economics, Beijing, China BGP INC., China National Petroleum Corporation

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 489-496

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）