Numerical solution for high order differential equations using a hybrid neural network—Optimization method

This paper reports a novel hybrid method based on optimization techniques and neural networks methods for the solution of high order ordinary differential equations. Here neural networks is considered as a part of large field called neural computing or soft computing. This means that we propose a ne...

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Bibliographic Details
Published in:	Applied mathematics and computation Vol. 183; no. 1; pp. 260 - 271
Main Authors:	Malek, A., Shekari Beidokhti, R.
Format:	Journal Article
Language:	English
Published:	New York, NY Elsevier Inc 01.12.2006 Elsevier
Subjects:	Applied sciences Computer science; control theory; systems Exact sciences and technology Feed forward artificial neural networks Global analysis, analysis on manifolds Language theory and syntactical analysis Mathematical analysis Mathematics Multidimensional optimization Nelder–Mead method Numerical analysis Numerical analysis. Scientific computation Ordinary differential equations Sciences and techniques of general use Theoretical computing Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds Multidimensional optimization Nelder–Mead method Ordinary differential equations Feed forward artificial neural networks Mixed distribution Neural computation Differential equation Initial value problem Optimization method Numerical method Neural network Nelder-Mead method Computing Many-dimensional calculations Partial differential equation Discretization method Boundary value problem Applied mathematics Numerical solution Mathematical programming
ISSN:	0096-3003, 1873-5649
Online Access:	Get full text
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