Identification/Prediction Algorithms for Armax Models with Relaxed Positive Real Conditions
John B. Moore, Maciej Jan Niedźwiecki, Lige Xia · Birkhäuser Boston eBooks · 1990
Extended least squares (ELS) algorithms are proposed for ARMAX model identification with the objective of avoiding the positive real condition associated with standard equation error and output error algorithms. This is achieved by an overparametrization at the cost of additional richness requirements on excitation signals, but without introducing ill-conditioning or infinite dimensional calculations as in earlier methods. Results for the case of D-step-ahead prediction ELS algorithms for ARMAX models also explored in the paper.