A Novel Three-Stage Filtering Identification Algorithm for the Exponential Autoregressive Time-Series Model

This letter studies the identification of a typical nonlinear time-series model, i.e., the exponential autoregressive model with unknown time-delay and colored noise. To deal with the identification difficulties caused by the time-delay, the identification model is transformed into an augmented one according to the redundant rule. Then, by means of the data filtering technique and the hierarchical principle, a novel three-stage filtering identification frame is presented to decrease the effect of the colored noise and complex nonlinearity. Moreover, a multi-innovation identification algorithm is proposed to improve the data utilization and estimation accuracy, and to jointly estimate the unknown model parameters and time-delay. The effectiveness of the proposed algorithm is evaluated by a numerical example.