Multiple-prediction-horizon recursive identification of hidden Markov models

This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard methods (which do not ensure consistent parameter estimation).