کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
411169 | 679184 | 2007 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A neural network learning algorithm of chemical process modeling based on the extended Kalman filter
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
The extended Kalman filtering (EKF) algorithm instead of the error back-propagation (BP) algorithm is used to train artificial neural networks (ANNs) for chemical process modeling. The basic idea is, by modifying the EKF gain, to prevent overfitting or filtering divergence phenomenon caused by outliers in the training samples. The EKF-based ANNs training method proposed is also applied to estimate the conversion rate in the polyacrylonitrile production process. Numerical simulations show that the modified EKF algorithm is superior to the BP algorithm in resisting noise and outliers, as well as generalization.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 4–6, January 2007, Pages 625–632
Journal: Neurocomputing - Volume 70, Issues 4–6, January 2007, Pages 625–632
نویسندگان
Huizhong Yang, Jiang Li, Feng Ding,