文献类型：期刊文献

英文题名：Parameter estimation of the exponentially damped sinusoids signal using a specific neural network

作者：Xiao, Xiuchun[1,2];Lai, Jian-Huang[2,4];Wang, Chang-Dong[3,4]

机构：[1]Guangdong Ocean Univ, Coll Informat, Zhanjiang 524025, Peoples R China;[2]Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China;[3]Sun Yat Sen Univ, Sch Mobile Informat Engn, Zhuhai 519082, Peoples R China;[4]SYSU CMU Shunde Int Joint Res Inst JRI, Shunde 528300, Peoples R China

年份：2014

卷号：143

起止页码：331

外文期刊名：NEUROCOMPUTING

收录：SCI-EXPANDED(收录号：WOS:000340982800033)、、EI(收录号：20143118007277)、Scopus(收录号：2-s2.0-84920918492)、WOS

基金：This work was supported by NSFC (61173084), National Science & Technology Pillar Program (No.: 2012BAK16B06), and Research Training Program of SMIE of Sun Yat-sen University. The authors would like to thank all the reviewers (including the reviewers of IScIDE 2013) for their comments which are very helpful in extending and revising the paper.

语种：英文

外文关键词：Exponentially damped sinusoids (EDSs) signal; Neural network; Levenberg-Marquardt algorithm; Parameter estimation

外文摘要：The problem of estimating the parameters of exponentially damped sinusoids (EDSs) signal has received very much attention in many fields. In this paper, a specific neural network termed EDSNN for parameter estimation of the EDSs has been proposed. Aiming at effectively evaluating the parameters of the EDSs signal, we construct a specific topology of EDSNN strictly following the mathematic formulation of EDSs signal. Then, what should be further done is how to train EDSNN using the data-set sampled from the EDSs signal. For this purpose, a modified Levenberg-Marquardt algorithm is derived for iteratively solving the weights of EDSNN by optimizing the pre-defined objective function. Profiting from good performance in fault tolerance of neural network, the proposed algorithm possesses a good performance in resistance to noise. Several computer simulations have been conducted to apply this method to some EDSs signal models. The results substantiate that the proposed EDSNN can synchronously obtain a higher precision for the damped factors, frequencies, also amplitudes and initial phases of all the EDSs than the state-of-the-art algorithm for noise free or noise case. (C) 2014 Elsevier B.V. All rights reserved.