Parallel identifiers for parameter estimation of strongly disturbed ARMA-processes

Several output error (or parallel) identifiers for parametric identification of discrete time autoregressive, moving-average (ARMA) systems with low signal-to-noise ratio were studied. An additional identification difficulty thereby was the estimation from a few number of data. Two kinds of adaptive recursive methods - model reference adaptive system algorithms (M.R.A.S.) and hyperstable adaptive recursive identifiers (HARF, e.g.) - were tested in simulation runs. The results are compared with an off-line (iterative) output error method and discussed. As a special case study modelling of human electroencephalogram (EEG) data is presented.