On adaptive HMM state estimation
On adaptive HMM state estimation
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DOI：
10.1109/78.655431
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发表时间：
1998-02-01
影响因子：
5.4
通讯作者：
Moore, JB
中科院分区：
文献类型：
--
作者：
Ford, JJ;Moore, JB
本文基于扩展最小二乘（ELS）概念和递归预测误差（RPE）方法，提出了一种新的在线自适应隐马尔可夫模型（HMM）状态估计方法。更适合马尔可夫模型，然后传统上用于识别线性系统。这些新的计划学习的一组N马尔可夫链状态，和后验概率在每个状态在每个时刻，他们的目的是实现的优势，在计算工作量和收敛速度，每两个类的早期提出的自适应HMM计划，而没有在这些领域的弱点，计算工作量是N阶的建议算法的实施方面进行了讨论，和模拟研究，以说明收敛速度相比，早期提出的在线计划。
In this paper new online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods, The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models then traditionally used in identification of linear systems.These new schemes learn the set of N Markov chain states, and the a posteriori probability of being in each of the states at each time instant, They are designed to achieve the strengths, in terms of computational effort and convergence rates, of each of the two classes of earlier proposed adaptive HMM schemes without the weaknesses of each in these areas, The computational effort is of order N.Implementation aspects of the proposed algorithms are discussed, and simulation studies are presented to illustrate convergence rates in comparison to earlier proposed online schemes.