1) Multiple Observations
多观测序列
1.
The Baum-Welch Algorithm of HMM2 with Multiple Observations;
多观测序列HMM2的Baum-Welch算法
2.
Results It proposes the structure of second-order HMM(HMM2) on condition that observation noise is not independent of the Markov chain,and obtain the Baum-Welch algorithm of the model on condition that multiple observations is not independent.
结果给出了在观测噪声和马尔可夫链不相互独立条件下二阶隐马尔可夫模型(second-or-der HMM:HMM2)的结构,获得了在多观测序列不相互独立的情况下HMM2的Baum-Welech学习算法。
2) observation sequence
观测序列
3) multiple observations
多观察序列
1.
This paper proposes a HMM training algorithm which is based on grouping multiple observations by multiple correlation coefficient .
在用多观察序列训练HMM理论的基础上,提出了一种基于对多观察序列按多相关系数分组的HMM训练算法(简称基于多相关分组的HMM训练算法)。
2.
This paper proposes a theoretical justification of the multiple observations HMMs training algorithm that does not impose the observation independence assumption and shows that the traditional BaumWelch algorithm is only a spec.
本文在不附加任何假设的前提下,提出了一种用多观察序列训练HMM的算法,从理论上解决了上述问题。
4) observation value sequence
观测值序列
5) measured displacement time series
位移观测序列
1.
For the measured displacement time series of rock mass engineering, the procedure for calculating the largest Lyapunov exponent is given on basis of phase space reconstruction.
首先,对于主运旧巷断面的收敛位移观测序列,采用插值方法对其进行了预处理,以获得等间隔的位移序列;然后,对该位移序列,利用上述混沌分析方法,获得其最大Lyapunov特征指数为0。
6) noisy observational series
受扰观测序列
1.
By exploiting the inherent properties of chaotic system and making use of the underlying information contained in the noisy observational series,a new method for estimating the sensitive parameters only from the noisy observational series of a state variable was developed.
针对高维非双曲线型混沌系统敏感参数难以估计的问题,在充分挖掘非线性系统及其观测序列本身特性的基础上,提出了一种由单状态变量受扰观测序列估计其系统敏感参数的新方法,有效的解决了由正的李雅谱诺夫指数所引起的误差扩散问题。
补充资料：大坝内部变形观测(见水工建筑物变形观测)
大坝内部变形观测(见水工建筑物变形观测)
daba neibubianxing guanCe大坝内部变形观测见水工建筑物变形观测。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条