دراسة تأثير تغييرات بارامترات الشبكة العصبونية على أدائها

Authors

  • كندة أبو قاسم
  • بلال شيحا
  • منذر سكيف

Abstract

تم  في هذا البحث دراسة شبكة عصبونية ذات ثلاث طبقات وتصميمها،  شبكة قادرة على تعلّم مجموعة معطيات كبيرة بمساعدة طريقة خطأ الانتشار العكسي، ودراسة تأثير تغيير البارامترات ( خطوة التعلّم, عدد العقد, نوع تابع التفعيل من أجل عدد من إشارات الدخل المختلفة ) والأثر الشديد الذي يسببه هذا التغيير في عمل الشبكة العصبونية، كما برهنت نتائج هذه التجارب على الحساسية الشديدة لاستجابة الشبكة العصبونية المصممة التي تعتمد على تقنية الانتشار العكسي لتغير هذه البارامترات.

 

This research aims at studying and designing a three-layer neural network, capable of dealing with massive data, relying on the back-propagation error method. It also explores the effect of parameters-change (learning step, number of nodes, type of activation function for a few different input signals) and the major impact, caused by this change on the neural network performance.

The results of these experiments have demonstrated the extreme sensitivity of the designed network which depends on the back-propagation mechanism to change these parameters.

 

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Published

2019-01-29

How to Cite

1.
أبو قاسم ك, شيحا ب, سكيف م. دراسة تأثير تغييرات بارامترات الشبكة العصبونية على أدائها. Tuj-eng [Internet]. 2019Jan.29 [cited 2025Jan.30];31(1). Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/7126

Issue

Vol. 31 No. 1 (2009): العلوم الهندسية

Section

Articles

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Copyright (c) 2019 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

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