Open AccessApproximation Solution of Fractional Partial Differential Equations by Neural Networks
Author(s) -
Adel Almarashi
Publication year - 2011
Publication title -
advances in numerical analysis
Language(s) - English
Resource type - Journals
eISSN - 1687-9570
pISSN - 1687-9562
DOI - 10.1155/2012/912810
Subject(s) - mathematics , partial differential equation , convergence (economics) , artificial neural network , mathematical analysis , fractional calculus , domain (mathematical analysis) , partial derivative , boundary value problem , radial basis function , numerical partial differential equations , first order partial differential equation , variable (mathematics) , differential equation , method of characteristics , computer science , machine learning , economics , economic growth
Neural networks with radial basis functions method are used to solve a class of initial boundary value of fractional partial differential equations with variable coefficients on a finite domain. It takes the case where a left-handed or right-handed fractional spatial derivative may be present in the partial differential equations. Convergence of this method will be discussed in the paper. A numerical example using neural networks RBF method for a two-sided fractional PDE also will be presented and compared with other methods
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom