基于多模型的不等长序列数据关联算法

单模型在处理不等长序列数据关联时不能兼顾计算精度、复杂度和抗扰性,为此提出了基于多模型(MM)的不等长序列数据关联算法。将基于滑动窗口和动态时间弯曲(DTW)的不等长序列相似度度量模型作为MM的输入模型,以2种模型计算得到的时似变化比作为模型判断指标进行模型转换,实现了2种模型的优势互补,并得到模型的应用条件,最后输出MM作用后的不等长序列相似度,以此作为关联指标进行关联判定。仿真实验验证了MM关联算法在处理不等长序列数据关联的有效性,并对序列长度和突变率变化对关联效果的影响进行了分析。

Data association algorithm for unequal length sequence based on multiple model

When dealing with data association for unequal length sequence,single model cannot balance computational accuracy,complexity and disturbance rejection.So a data association algorithm for unequal length sequence based on multiple model (MM) was proposed.The two unequal length sequence similarity measurement model based on sliding window and dynamic time warping (DTW) were selected as the input model of MM,which uses the rate of change between time and similarity of two model as the index to realize the transformation of the two models.It combines both advantages of two models and gets the models' application condition.The unequal length sequence similarity is output after MM as the index to judge the association of the sequence data.Simulation results show the effectiveness of the proposed algorithm for unequal length sequence and analyze the effect of sequence length and fluctuant rate on association result.