摘要
该文将径向基函数网络引入地震数据处理中,实现了函数逼近法地震数据的插值处理,在实际地震数据处理中取得了较好的应用效果。主要研究了径向基函数网络的理论、方法、应用及其逼近性能。该网络充分地利用了包含在训练数据中的信息,可自适应地确定网络隐层节点数目、径向基函数中心以及网络的权系数,生成的网络具有规模小、收敛快和数值稳定等优点。对同一函数进行逼近且精度相同时,径向基函数网络所用时间远远小于BP网络,因此是有广阔应用前景的一种新型神经网络。
This paper introduces the radial basis function （RBF） network in the aeismic data processing, and realizes the inserting data in seismic data processing with function approximation method. This method brought satisfactory result when applied to real seismic data. The dissertation mainly studies the theory, methods, applications of radial basis function networks and the approximation capability. The network can sufficiently utilize the information contained in the training data, choose the centers of radial basis functions and the weights of networks one by one until an adequate network has been constructed, providing a simple and efficient means for growing radial basis function net-works. Therefore, radial basis function network is a sort of new-fashioned neural network with wide application foreground.
出处
《计算机仿真》
CSCD
2005年第7期54-56,共3页
Computer Simulation
关键词
地震数据处理
地震勘探
神经网络
径向基函数网络
函数逼近
Seismic data processing
Seismic exploration
Neural networks
Radial basis function networks
Function approximation
