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Linear and nonlinear ARMA model parameter estimation using an artificial neural networkThis paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.
Document ID
Document Type
Reprint (Version printed in journal)
External Source(s)
Chon, K. H. (Massachusetts Institute of Technology Cambridge 02139, United States)
Cohen, R. J.
Date Acquired
August 22, 2013
Publication Date
March 1, 1997
Publication Information
Publication: IEEE transactions on bio-medical engineering
Volume: 44
Issue: 3
ISSN: 0018-9294
Subject Category
Life Sciences (General)
Funding Number(s)
Distribution Limits
NASA Discipline Regulatory Physiology
Non-NASA Center