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p-Index From 2021 - 2026
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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Alaa Luqman Ibrahim
University of Zakho
Computer Science & IT

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A new three-term conjugate gradient method for training neural networks with global convergence Alaa Luqman Ibrahim; Mohammed Guhdar Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp551-558

Abstract

Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG) method has been tested. The results of numerical experiences on some wellknown test function shown that our new modified method is very effective, by relying on the number of functions evaluation and number of iterations, also included the numerical results for training feed-forward neural networks with other well-known method in this field.
Two new classes of conjugate gradient method based on logistic mapping Banaz Hamza Jahwar; Alaa Luqman ibrahim; Sherzad Muhammed Ajeel; Salah Gazi Shareef
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 1: February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i1.25264

Abstract

Following the standard methods proposed by Polak-Ribiere-Polyak (P-R), in this work we introduce two new non-linear conjugate gradient methods for solving unconstraint optimization problem, our new methods based on P-R. Standard method (P-R) have performance well in numerical result but does not satisfy global convergency condition. In this paper we modified double attractive and powerful parameters that have better performance and good numerical result than P-R method, also each of our robust method can satisfies the descent condition and global convergency condition by using wolf condition. More over the second method modified by logistic mapping form, the main novelty is their numerical results and demonstrate performance well with compare to a standard method.
Co-Authors Banaz Hamza Jahwar Mohammed Guhdar Mohammed Salah Gazi Shareef Sherzad Muhammed Ajeel