随着已知的最大峰值移位，动态时间扭曲的归一化约束模式提供了最好的结果。
With prior knowledge of the maximum peak shifting, dynamic time warping in a normalized constrained mode provides the best performance.
为提高电压扰动信号分类识别的精度，提出了一种基于数学形态学与动态时间扭曲的新算法。
A novel algorithm based on the mathematical morphology and the dynamic time warping was proposed for improving the accuracy of voltage disturbance classification.
为提高电压扰动信号分类识别的精度，提出了一种基于数学形态学与动态时间扭曲的新算法。
A novel algorithm based on the mathematical morphology and the dynamic time warping was proposed for improving the accuracy of voltage disturbance classification.
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