Numerical solution of a Fredholm integro-differential equation modelling θ ˙ -neural networks

We propose several approaches to the numerical solution of a new Fredholm integro-differential equations modelling neural networks. A solution strategy based on expansions onto standard cardinal basis functions and collocation is presented. Comparative numerical experiments illustrate specific advan...

Full description

Saved in:
Bibliographic Details
Published inApplied mathematics and computation Vol. 195; no. 2; pp. 523 - 536
Main Authors Jackiewicz, Z., Rahman, M., Welfert, B.D.
Format Journal Article
LanguageEnglish
Published New York, NY Elsevier Inc 01.02.2008
Elsevier
Subjects
Approximations and expansions
Collocation
Exact sciences and technology
Fredholm integro-differential equation
Gaussian quadrature
Integral equations
Mathematical analysis
Mathematics
Neural networks
Numerical analysis
Numerical analysis. Scientific computation
Piecewise linear approximation
Polynomial approximation
Pseudospectral methods
Rational approximation
Sciences and techniques of general use
Fredholm integro-differential equation
Rational approximation
Pseudospectral methods
Collocation
Piecewise linear approximation
Neural networks
Polynomial approximation
Gaussian quadrature
Numerical integration
Neural network
Fredholm equation
Numerical analysis
Pseudospectral method
Applied mathematics
Numerical solution
Linear approximation
Integrodifferential equation
Online AccessGet full text

Cover

More Information
Summary:We propose several approaches to the numerical solution of a new Fredholm integro-differential equations modelling neural networks. A solution strategy based on expansions onto standard cardinal basis functions and collocation is presented. Comparative numerical experiments illustrate specific advantages and drawbacks of the different approaches and are used to motivate alternate strategies.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2007.05.031