کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
568086 | 876250 | 2011 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Radial Basis Function network training algorithm using a non-symmetric partition of the input space – Application to a Model Predictive Control configuration
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزار
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advances in Engineering Software - Volume 42, Issue 10, October 2011, Pages 830–837
Journal: Advances in Engineering Software - Volume 42, Issue 10, October 2011, Pages 830–837
نویسندگان
Alex Alexandridis, Haralambos Sarimveis, Konstantinos Ninos,