کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
387561 660905 2009 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving the generalization performance of RBF neural networks using a linear regression technique
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improving the generalization performance of RBF neural networks using a linear regression technique
چکیده انگلیسی

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 36, Issue 10, December 2009, Pages 12049–12053
نویسندگان
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