一类广义隐马尔科夫模型的建模与参数估计(英文)  

Modeling and Parameter Estimation of a Class of General Hidden Markov Model

作　　者：胡可[1] 张大力[1] 

机构地区：[1]中国科学技术大学自动化系,合肥230026

出　　处：《中国科学院研究生院学报》2005年第2期210-217,共8页Journal of the Graduate School of the Chinese Academy of Sciences

摘　　要：隐马尔科夫模型在很多方面已有广泛应用 .讨论了一类更为一般的模型 ,这类模型由WojciechPieczynski首次提出 ,并且给出了在图像识别中的应用 .这里首次给出在离散观测和离散状态下该模型的精确数学描述 ,其中包括建模、状态估计和参数估计 。It is well-known that HMM has been widely used in many fields. In this paper we will discuss a more general model, which is similar to Pairwise Markov Model(PM M) proposed by Wojciech Pieczynski. Compared to HMM, the state process here is n ot necessarily a Markov chain. So it has more general applications in image segm entation, speech signal processing, and etc. We will give a complete mathematica l description for this model with discrete states and discrete observations, inc luding modeling, state estimation and parameter estimation, which haven't been s tudied before. Based on the method proposed here, we will get a recursive algori thm for the estimation of the state and the parameters.

关 键 词：测度变换 递归参数估计 递归状态估计 广义隐马尔科夫模型 

分 类 号：O211.62[理学—概率论与数理统计]