Optimal algorithm of data streams clustering on sliding window model;
提出了一种基于滑动窗口的综合语音端点检测方法。
A mathematical model for sliding window polynomial fitting is proposed.
提出了滑动窗多项式拟合数学模型。
A mathematical model that characterizes the affects of the updating cycle of sliding window and data stream rate on predictive accuracy is also presented.
还提出了滑动窗口的更新周期、数据流的流速对预测精度影响的数学模型。
Based on an analysis of EASI batch process algorithms for traditional blind source separation, a sliding window ICA algorithm is studied to deal with complex signals in the time variant mixing model.
通过对传统盲源分离批处理EASI算法的分析，针对时变信道中通信信号的复数形式，以平滑窗的形式实现了批处理算法在时变混合模型下的应用。
The results obtained by this method prove the validity of the Entropy Model of Sliding Window (EMSW) in other aspect.
用该模型得出的结果证实了滑动窗口信息熵模型得出的结果的正确性。
In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system.
该方法构造了滚动时间窗，利用滚动时间窗内的数据优化建模。
In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system.
该方法构造了滚动时间窗，利用滚动时间窗内的数据优化建模。
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