| Convergence Study in Extended Kalman Filter-based Training of Recurrent Neural Networks. | |
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MedLine Citation:
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PMID: 21402512 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Recurrent neural network (RNN) has emerged as a promising tool in modeling nonlinear dynamical systems, but the training convergence is still of concern. This paper aims to develop an effective extended Kalman filter-based RNN training approach with a controllable training convergence. The training convergence problem during extended Kalman filter-based RNN training has been proposed and studied by adapting two artificial training noise parameters: the covariance of measurement noise (R) and the covariance of process noise (Q) of Kalman filter. The R and Q adaption laws have been developed using the Lyapunov method and the maximum likelihood method, respectively. The effectiveness of the proposed adaption laws has been tested using a nonlinear dynamical benchmark system and further applied in cutting tool wear modeling. The results show that the R adaption law can effectively avoid the divergence problem and ensure the training convergence, whereas the Q adaption law helps improve the training convergence speed. |
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Authors:
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Xiaoyu Wang; Yong Huang |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-3-10 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: - ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2011 Mar |
Date Detail:
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Created Date: 2011-3-15 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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