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Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate
المؤلفون المشاركون
Luan, Tiantian
Sun, Mingxiao
Chen, Daidai
Xia, Guoqing
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-22
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
The neural network has the advantages of self-learning, self-adaptation, and fault tolerance.
It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns.
However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved.
This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network.
The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer.
This method effectively solves the problem that the RBF neural network structure is too large or too small.
The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm.
At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network.
Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability.
The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Luan, Tiantian& Sun, Mingxiao& Xia, Guoqing& Chen, Daidai. 2018. Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate. Complexity،Vol. 2018, no. 2018, pp.1-19.
https://search.emarefa.net/detail/BIM-1135558
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Luan, Tiantian…[et al.]. Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate. Complexity No. 2018 (2018), pp.1-19.
https://search.emarefa.net/detail/BIM-1135558
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Luan, Tiantian& Sun, Mingxiao& Xia, Guoqing& Chen, Daidai. Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate. Complexity. 2018. Vol. 2018, no. 2018, pp.1-19.
https://search.emarefa.net/detail/BIM-1135558
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1135558
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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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
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