Robust adaptive Markov decision processes in multi-vehicle applications
ACC'09 Proceedings of the 2009 conference on American Control Conference
Relative entropy rate based multiple hidden Markov model approximation
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
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This paper presents new schemes for recursive estimation of the state transition probabilities for hidden Markov models (HMMs) via extended least squares (ELS) and recursive state prediction error (RSPE) methods. Local convergence analysis for the proposed RSPE algorithm is shown using the ordinary differential equation (ODE) approach developed for the more familiar recursive output prediction error (RPE) methods. The presented scheme converges and is relatively well conditioned compared with the previously proposed RPE scheme for estimating the transition probabilities that perform poorly in low noise. The ELS algorithm presented is computationally of order N2, which is less than the computational effort of order N4 required to implement the RSPE (and previous RPE) scheme, where N is the number of Markov states. Building on earlier work, an algorithm for simultaneous estimation of the state output mappings and the state transition probabilities that requires less computational effort than earlier schemes is also presented and discussed. Implementation aspects of the proposed algorithms are discussed, and simulation studies are presented to illustrate the convergence and convergence rates