کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
392573 664778 2016 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fuzzy nonlinear regression analysis using a random weight network
ترجمه فارسی عنوان
تجزیه و تحلیل رگرسیون غیرخطی فازی با استفاده از شبکه وزن تصادفی
کلمات کلیدی
مجموعه برش α ؛ فازی درون فازی بیرون ؛ رگرسیون غیرخطی فازی؛ شبکه وزن تصادفی؛ عدد فازی مثلثی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Modeling a fuzzy-in fuzzy-out system where both inputs and outputs are uncertain is of practical and theoretical importance. Fuzzy nonlinear regression (FNR) is one of the approaches used most widely to model such systems. In this study, we propose the use of a Random Weight Network (RWN) to develop a FNR model called FNRRWN, where both the inputs and outputs are triangular fuzzy numbers. Unlike existing FNR models based on back-propagation (BP) and radial basis function (RBF) networks, FNRRWN does not require iterative adjustment of the network weights and biases. Instead, the input layer weights and hidden layer biases of FNRRWN are selected randomly. The output layer weights for FNRRWN are calculated analytically based on a derived updating rule, which aims to minimize the integrated squared error between α-cut sets that correspond to the predicted fuzzy outputs and target fuzzy outputs, respectively. In FNRRWN, the integrated squared error is solved approximately by Riemann integral theory. The experimental results show that the proposed FNRRWN method can effectively approximate a fuzzy-in fuzzy-out system. FNRRWN obtains better prediction accuracy in a lower computational time compared with existing FNR models based on BP and RBF networks.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 364–365, 10 October 2016, Pages 222–240
نویسندگان
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