LINEAR AND NONLINEAR ARMA MODEL PARAMETER-ESTIMATION USING AN ARTIFICIAL NEURAL-NETWORK

This paper addresses parametric system identification of linear and no nlinear dynamic systems by analysis of the input and output signals, S pecifically, 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 poly nomial 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 simu lations, Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simula ted data or by conventional least squares ARMA analysis, The feasibili ty of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measu rements of heart rate (HR) and instantaneous lung volume (ILV) fluctua tions.