On Neural Network Algorithms for Solving Non- Linear Problems

Gulnar Sadq

ISBN 10: 3846580805 / ISBN 13: 9783846580806

Edité par LAP LAMBERT Academic Publishing Jan 2012, 2012

A propos de cet article

Neuware -First we describe, analyze and present the theoretical derivations and the source codes for several (modified and well-known) non-linear Neural Network algorithms based on the unconstrained optimization theory and applied to supervised training networks. In addition to the indication of the relative efficiency of these algorithms in an application, we analyze their main characteristics and present the MATLAB source codes. Algorithms of this part depend on some modified variable metric updates and for the purpose of comparison, we illustrate the default values specification for each algorithm, presenting a simple non-linear test problem. Further more in this thesis we also emphasized on the conjugate gradient (CG) algorithms, which are usually used for solving nonlinear test functions and are combined with the modified back propagation (BP) algorithm yielding few new fast training multilayer Neural Network algorithms. This study deals with the determination of new search directions by exploiting the information calculated by gradient descent as well as the previous search directions.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch.

N° de réf. du vendeur 9783846580806

Détails bibliographiques

• Titre: On Neural Network Algorithms for Solving Non- Linear Problems
• Éditeur: LAP LAMBERT Academic Publishing Jan 2012
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3846580805
• ISBN à 13 chiffres: 9783846580806
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

First we describe, analyze and present the theoretical derivations and the source codes for several (modified and well-known) non-linear Neural Network algorithms based on the unconstrained optimization theory and applied to supervised training networks. In addition to the indication of the relative efficiency of these algorithms in an application, we analyze their main characteristics and present the MATLAB source codes. Algorithms of this part depend on some modified variable metric updates and for the purpose of comparison, we illustrate the default values specification for each algorithm, presenting a simple non-linear test problem. Further more in this thesis we also emphasized on the conjugate gradient (CG) algorithms, which are usually used for solving nonlinear test functions and are combined with the modified back propagation (BP) algorithm yielding few new fast training multilayer Neural Network algorithms. This study deals with the determination of new search directions by exploiting the information calculated by gradient descent as well as the previous search directions.