تصميم وتنفيذ شبكة عصبية فائقة من نوع إلمان لنظام روبوتات SDN

محتوى المقالة الرئيسي

Shahad Jumaa Shaaban
Nadia Adnan Shiltagh Al-Jamali

الملخص

يُعدّ تخطيط المسار مهمة بالغة الأهمية في أنظمة الروبوتات ذاتية التشغيل، لا سيما في البيئات الديناميكية وغير المتوقعة. غالبًا ما تعاني خوارزميات التخطيط التقليدية من محدودية القدرة على التكيف وارتفاع التكلفة الحسابية في ظل قيود الوقت الفعلي. تقترح هذه الورقة البحثية إطار عمل ذكيًا للملاحة الروبوتية قائمًا على الشبكات المعرفة بالبرمجيات (SDN) ومدمجًا مع شبكة عصبية متعددة النبضات من نوع إلمان (MS-ENN) لتحسين أداء تخطيط المسار. يُقدّم النموذج المقترح آلية ترميز زمني متعددة النبضات تُتيح تمثيلًا أغنى لخصائص البيئة الديناميكية مقارنةً بالنماذج العصبية التقليدية. يجمع النظام بين مُخطط جبهة الموجة النبضية (SWP) لتوليد المسار الأولي وشبكة MS-ENN لاتخاذ القرارات والتنبؤ التكيفي. تُظهر النتائج التجريبية أن النهج المقترح يُحسّن كفاءة المسار، ويُقلّل وقت التخطيط، ويزيد معدل النجاح في كلٍ من السيناريوهات الثابتة والديناميكية. تُبرز هذه النتائج فعالية دمج الشبكات المعرفة بالبرمجيات مع بنى الشبكات العصبية النبضية المتقدمة للملاحة الروبوتية الذكية.

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تفاصيل المقالة

القسم

Articles

كيفية الاقتباس

"تصميم وتنفيذ شبكة عصبية فائقة من نوع إلمان لنظام روبوتات SDN" (2026) مجلة الهندسة, 32(7), ص 98–112. doi:10.31026/j.eng.2026.07.05.

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