| 基于周期的一阶隐马尔可夫模型及其学习算法 |
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| 引用本文： | 田琳芳,王琤,黄自萍.基于周期的一阶隐马尔可夫模型及其学习算法[J].计算机工程与科学,2009,31(12). |
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| 作者姓名： | 田琳芳 王琤 黄自萍 |
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| 作者单位： | 田琳芳,王琤(同济大学数学系,上海,200092);黄自萍(同济大学中德学院,上海,200092) |
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| 摘 要： | 对于周期变动明显的数据,本文通过引入周期状态转移矩阵,提出了一种基于周期的一阶隐马尔可夫模型,分析并给出了该模型的似然计算、隐状态估计和模型训练算法。最后的数值实验表明,该模型能有效提高预测的精确度和模型的似然度,并加快模型训练的收敛速度。
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| 关 键 词： | 隐马尔可夫模型(HMM) 熵 周期状态转移矩阵 时间序列 最小二乘法 |
The First-Order Hidden Markov Model Based on Periodicity and Its Learning Algorithm |
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| Abstract: | By introducing a periodic state transfer matrix, a first-order HMM model based on periodicity is proposed for the data with explicit periodicity. The algorithms, including likelihood computation, hidden state estimation and model training, are also presented and analyzed. Finally, numerical simulation experiments show that the new method efficiently improves the prediction precision and the model likelihood. Moreover, it also increases the convergence rate of model training. |
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| Keywords: | Hidden Markov Model entropy periodic state transfer matrix time series least square method |
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