Identification of Hidden Markov Models with Grouped State Values

Abstract

This paper presents a recursive prediction error algorithm which addresses the problem of computational complexity for on-line identification of hidden Markov models (HMMs). The particular class of HMMs considered has state-values which are clustered into groups. This allows a reformulation of the Markov model and results in a sub-optimal reduced order identification scheme. Actually, an exact definition of clustering is not discussed, rather, a general identification technique is presented for which the computational requirements are greatly reduced when the state-values are divided in some way into groups. The applicability to certain types of cluster patterns is tested via simulation studies.