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Robust nonlinear ARMA model parameter estimation using a stochastic recurrent neural network

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1 Author(s)
Chon, K.H. ; Dept. of Electr. Eng., City Coll. of New York, NY, USA

We introduce a new approach for estimating linear and nonlinear stochastic ARMA model parameters, using recurrent neural networks. This new approach is a 2-step approach in which the parameters of the deterministic part of stochastic ARMA parameters are first estimated via a three-layer network and then re-estimated using the prediction error as one of the inputs to the networks. Using this simple two-step procedure, we obtain more robust model predictions than the deterministic network approach despite the presence of significant amounts of either dynamic or additive noise in the output signal. A comparison between the deterministic and stochastic approaches is made using renal blood pressure and flow data

Published in:

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint  (Volume:2 )

Date of Conference:

Oct 1999