On Neural Network Algorithms for Solving Non- Linear Problems

Abdulla Sulaiman Najmaddin Sadq Gulnar Younis Al-Bayati Abbas

ISBN 10: 3846580805 / ISBN 13: 9783846580806

Published by Editorial Academica Espanola, 2012

About this Item

PRINT ON DEMAND pp. 156.

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Bibliographic Details

• Title: On Neural Network Algorithms for Solving Non- Linear Problems
• Publisher: Editorial Academica Espanola
• Publication Date: 2012
• Language: English
• ISBN 10: 3846580805
• ISBN 13: 9783846580806
• Binding: Soft cover
• Condition: New

About this title

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.

About the Author

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.