Training of fuzzy neural networks via quantum-behaved particle swarm optimization and rival penalized competitive learning
المؤلف
المصدر
The International Arab Journal of Information Technology
العدد
المجلد 9، العدد 4 (31 يوليو/تموز 2012)، ص ص. 306-313، 8ص.
الناشر
تاريخ النشر
2012-07-31
دولة النشر
الأردن
عدد الصفحات
8
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
الملخص EN
There are some difficulties encountered in the application of fuzzy Radial Basis Function (RBF) neural network.
One of them is how to determine the number of hidden rule neurons and another difficulty is about interpretability.
In order to overcome these difficulties, we have proposed a fuzzy neural network based on RBF network and tankage surgeon fuzzy system.
We have used a new structure of fuzzy RBF neural network, which has been proved that it is better than other structures in term of interpretability.
Our model also use a Rival Penalized Competitive Learning (RPCL) and a swarm based algorithm called Quantum-behaved Particle Swarm Optimization (QPSO) to determine design parameters of hidden layer and design parameters of output layer, respectively.
RPCL is the best clustering algorithm that is introduced so far.
The Particle Swarm Optimization (PSO) is a well-known population-based swarm intelligence algorithm.
The QPSO is also proposed by combining the classical CPSO philosophy and quantum mechanics to improve performance of PSO.
We have compared the performance of the proposed method with gradient based method.
Simulation results of nonlinear function approximation demonstrate the superiority of the proposed method over gradient based method.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Farzi, Said. 2012. Training of fuzzy neural networks via quantum-behaved particle swarm optimization and rival penalized competitive learning. The International Arab Journal of Information Technology،Vol. 9, no. 4, pp.306-313.
https://search.emarefa.net/detail/BIM-305182
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Farzi, Said. Training of fuzzy neural networks via quantum-behaved particle swarm optimization and rival penalized competitive learning. The International Arab Journal of Information Technology Vol. 9, no. 4 (Jul. 2012), pp.306-313.
https://search.emarefa.net/detail/BIM-305182
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Farzi, Said. Training of fuzzy neural networks via quantum-behaved particle swarm optimization and rival penalized competitive learning. The International Arab Journal of Information Technology. 2012. Vol. 9, no. 4, pp.306-313.
https://search.emarefa.net/detail/BIM-305182
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 313
رقم السجل
BIM-305182
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر