Numerical solution of differential equations by radial basis function neural networks

被引：7

作者：

Li, JY ^{[1
]}

Luo, SW ^{[1
]}

Qi, YJ ^{[1
]}

Huang, YP ^{[1
]}

机构：

[1] No Jiaotong Univ, Inst Comp Sci, Beijing 100044, Peoples R China

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

radial basis function networks; solution of differential equation; multiquadric;

D O I：

10.1109/IJCNN.2002.1005571

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a method for solving linear ordinary differential equations (ODEs) based on multiquadric (MQ) radial basis function networks (RBFNs). According to the thought of approximation of function and/or its derivatives by using radial basis function networks [1]-[3], another new RBFN approximation procedures different from [1] are developed in this paper for solving ODES. This technique can determine all the parameters at the same time without a learning process. The advantage of this technique is that it doesn't need sufficient data, just relies on the domain and the boundary. Our results are more accurate.

引用

页码：773 / 777

页数：5

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