Title :
On a Neural Approximator to ODEs
Author :
Filici, Cristian
Author_Institution :
Univ. de Buenos Aires, Buenos Aires
fDate :
3/1/2008 12:00:00 AM
Abstract :
The object of this brief is to present and analyze the training of a single-layer neural network in order to solve ordinary differential equations (ODEs). Properties of the approximator are derived and some examples of its application are shown.
Keywords :
approximation theory; differential equations; learning (artificial intelligence); mathematics computing; neural nets; neural approximator; ordinary differential equation; single-layer neural network training; Approximation error; Artificial neural networks; Cost function; Differential equations; Finite difference methods; Neural networks; Partial differential equations; Stability; Vectors; Error bound; neural network; ordinary differential equation (ODE); stability; Algorithms; Animals; Humans; Models, Neurological; Neural Networks (Computer); Nonlinear Dynamics;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.915109
