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• W3146625735 abstract "To address the problem of low filtering accu- racy and divergence caused by unknown process noise statistics and local linearization in neural network state- space model, this paper proposes an adaptive process noise covariance particle filter algorithm for the radial basis function (RBF) networks. Using the algorithm, the evolu- tion of the weights and centers of RBF networks is achieved sequentially in time by use of the extended Kal- man particle filter algorithm, and the process noise covariance matrices are also obtained simultaneously by maximizing the evidence density function with respect to the process noise covariance matrices. Performance of the presented approach is evaluated by two function approxi- mation problems. Experimental results show that the pro- posed approach obtains better prediction accuracy than other well-known training algorithms." @default.
• W3146625735 created "2021-04-13" @default.
• W3146625735 creator A5010537547 @default.
• W3146625735 creator A5041506575 @default.
• W3146625735 date "2014-01-01" @default.
• W3146625735 modified "2023-09-27" @default.
• W3146625735 title "A sequential learning algorithm based on adaptive particle filtering for RBF networks" @default.
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• W3146625735 hasPublicationYear "2014" @default.
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