Learning Radial Basis Function Networks with the Trust Region Method for Boundary Problems

被引：0
|
作者：
L. N. Elisov
V. I. Gorbachenko
M. V. Zhukov
机构：
[1] Moscow State Technical University of Civil Aviation,
[2] Penza State University,undefined
来源：
Automation and Remote Control | 2018年 / 79卷
关键词：
boundary value problems of mathematical physics; radial basis function networks; learning of neural networks; method of trust region;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We consider the solution of boundary value problems of mathematical physics with neural networks of a special form, namely radial basis function networks. This approach does not require one to construct a difference grid and allows to obtain an approximate analytic solution at an arbitrary point of the solution domain. We analyze learning algorithms for such networks. We propose an algorithm for learning neural networks based on the method of trust region. The algorithm allows to significantly reduce the learning time of the network.
引用
收藏
页码：1621 / 1629
页数：8
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