دانلود مقاله ISI انگلیسی شماره 70739
عنوان فارسی مقاله

روش تشخیص خطا برای شبکه Ad-hoc موبایل با استفاده از شبکه های عصبی Smart ☆

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
70739 2014 6 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Fault Diagnosis Method for Mobile Ad-hoc Network by Using Smart Neural Networks ☆
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Procedia Computer Science, Volume 42, 2014, Pages 222–227

کلمات کلیدی
شبکه های عصبی؛ آتاماتای یادگیر - MANETs؛ سیستم های عیب یابی خطا
پیش نمایش مقاله
پیش نمایش مقاله روش تشخیص خطا برای شبکه Ad-hoc موبایل با استفاده از شبکه های عصبی Smart ☆

چکیده انگلیسی

MANETs are dynamic collection of autonomous nodes that communicate with each other via wireless connections. One of the events that the network should have expected it to be a fault, and the behavior is more important, in this network. So that fault diagnosis can effect on final performance of the network in such a way that it does not fall under the negative impact of the fault. A non-linear neural network is a statistical method for modeling data or the tools to make decisions. Artificial neural network is a method for pattern recognition and classification. Error detection is a problem of categorization or classification. The use of neural networks can be useful in fault diagnosis in MANETs because of fault diagnosis is a classification problem. But one problem with this method is placed in a local optimum. Here a method based on the combination of the back-propagation algorithm, a local search algorithm and learning automata as efficient global search, is proposed. In the proposed method, the algorithm of learning automata adjusting learning rate on neural network according to given formula. For training and testing the neural network of the mobile network parameters that were measured, were used as input and output. The results show that the proposed method in terms of repeatability, reliability and lack of placement in a local optimum is better.

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