کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
409353 679068 2007 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm
چکیده انگلیسی

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustness of Kalman filter algorithm dead-zone robust modification is applied to Kalman filter. Lyapunov method is used to prove that the Kalman filter training is stable.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 13–15, August 2007, Pages 2460–2466
نویسندگان
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