On Neural Network Algorithms for Solving Non- Linear Problems: Optimizations and Non-linear programming - Couverture souple

Sadq, Gulnar; Younis Al-Bayati, Abbas; Abdulla Sulaiman, Najmaddin

 
9783846580806: On Neural Network Algorithms for Solving Non- Linear Problems: Optimizations and Non-linear programming
Couverture souple
ISBN 10 : 3846580805 ISBN 13 : 9783846580806
Editeur : LAP LAMBERT Academic Publishing, 2012

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

Gulnar Wasim Sadiq, was burn in 1974 kurdistan region. Complete the PhD. Degree at University of Sulaimani- College of Science, Department of Mathematics in the field Operation Research and Optimization.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3846580805
ISBN 13 
9783846580806
Reliure
Broché
Nombre de pages
156
Coordonnées du fabricant
non disponible
Personne responsable
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