Solving differential equations with Fourier series and Evolution Strategies

被引:22
作者
Chaquet, Jose M. [1 ]
Carmona, Enrique J. [1 ]
机构
[1] Univ Nacl Educ Distancia, Dpto Inteligencia Artificial, Escuela Tecn Super Ingn Informat, E-28040 Madrid, Spain
关键词
Differential equations; Fourier series; Evolution Strategies; Mesh-free methods; Harmonic analysis; NEURAL-NETWORKS; FLOW;
D O I
10.1016/j.asoc.2012.05.014
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:3051 / 3062
页数:12
相关论文
共 25 条
  • [1] [Anonymous], 2002, Cambridge Texts in Applied Mathematics, DOI [10.1017/CBO9780511791253, DOI 10.1017/CBO9780511791253]
  • [2] [Anonymous], P GEN EV COMP C GECC
  • [3] Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming
    Balasubramaniam, P.
    Kumar, A. Vincent Antony
    [J]. GENETIC PROGRAMMING AND EVOLVABLE MACHINES, 2009, 10 (01) : 71 - 89
  • [4] Beyer H.G., 2002, ATURAL COMPUTING, V1, P3
  • [5] Numerical solution of PDEs via integrated radial basis function networks with adaptive training algorithm
    Chen, Hong
    Kong, Li
    Leng, Wen-Jun
    [J]. APPLIED SOFT COMPUTING, 2011, 11 (01) : 855 - 860
  • [6] Eiben A.E., 2007, INTRO EVOLUTIONARY C
  • [7] An intelligent computing technique for fluid flow problems using hybrid adaptive neural network and genetic algorithm
    El-Emam, Nameer N.
    Al-Rabeh, Riadh H.
    [J]. APPLIED SOFT COMPUTING, 2011, 11 (04) : 3283 - 3296
  • [8] Farlow S. J., 1993, Partial differential equations for scientists and engineers
  • [9] Ghodadra B.L., 2010, ACTA MATH HUNG, V22, P187
  • [10] A hybrid genetic programming approach for the analytical solution of differential equations
    Kirstukas, SJ
    Bryden, KM
    Ashlock, DA
    [J]. INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, 2005, 34 (03) : 279 - 299