An RPCL-based approach for Markov model identification with unknown state number

摘要

This paper presents an alternative identification approach for the Markov model studied in Krishnamurthy and Moore (1993). Our approach estimates the state sequence and model parameters with the help of a clustering analysis by the rival penalized competitive learning (RPCL) algorithm (Xa 1996). Compared to the method in Krishnamurthy and Moore, this new approach not only extends the model from scalar states to multidimensional ones, but also makes the model identification with the correct number of states decided automatically. The experiments have shown that it works well.