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Erschienen in:
2013 | OriginalPaper | Buchkapitel
A Neural Network for Parameter Estimation of the Exponentially Damped Sinusoids
verfasst von : Xiuchun Xiao, Jian-Huang Lai, Chang-Dong Wang
Erschienen in: Intelligence Science and Big Data Engineering
Verlag: Springer Berlin Heidelberg
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The problem of estimating the parameters of exponentially damped sinusoids (EDSs) has received very much attention in many fields. Strictly following the mathematic formulation of EDSs, we construct a specific neural network, termed EDSNN. In order to train EDSNN, a modified Levenberg-Marquardt iterative algorithm is derived. Profiting from good performance in fault tolerance of neural network, the proposed algorithm can be expected to possess a good performance in resistance to noise to some extent. Computer simulations have been conducted to apply this method to some EDSs signal models. The results substantiate the proposed EDSNN can obtain a higher precision for the parameters of the EDS component than the state-of-the-art algorithm.