Numerical solution of Volterra–Fredholm integral equations based on ε-SVR method
作者:
Highlights:
•
摘要
In this paper, we try to study the numerical methods for solving integral equations from a new perspective—machine learning method. By means of the idea of kernel ε-support vector regression machine (ε-SVR), we construct an optimization modeling for a class of Volterra–Fredholm integral equations and propose a novel numerical method for solving them. The proposed method has a certain versatility and can be used to solve some other kinds of integral equations. In order to verify the effectiveness of the proposed method, we perform a series of comparative experiments with six specific Volterra–Fredholm integral equations and a method proposed in Wang et al. (2014). Experimental results show that the proposed method has a good approximation property.
论文关键词:Volterra–Fredholm integral equations,Numerical solution,Support vector regression,Trapezoid quadrature,Quadratic programming
论文评审过程:Received 30 April 2015, Revised 25 October 2015, Available online 23 December 2015, Version of Record 4 January 2016.
论文官网地址:https://doi.org/10.1016/j.cam.2015.12.002