Data Fusion and Pattern Classification in Dynamical Systems Via Symbolic Time Series Analysis
Data Fusion and Pattern Classification in Dynamical Systems Via Symbolic Time Series Analysis
复制标题
通过符号时间序列分析进行动态系统中的数据融合和模式分类
DOI：
10.1115/1.4062830
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ray, Asok
中科院分区：
文献类型：
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作者：
Chen, Xiangyi;Ray, Asok
符号时间序列分析(STSA)在连续演化动态系统的研究中起着重要的作用，能够解释多个传感器信号的联合效应是充分表达嵌入知识的关键。本技术简介开发并通过仿真验证了一种基于STSA的算法，该算法可以从多传感器时间序列数据的集合中对动态系统做出及时的决策，以进行信息融合和模式分类。在这种情况下，最常用的方法之一是各种结构的神经网络(NN)；然而，这些基于NN的方法可能需要大量的数据和较长的计算时间来进行训练。另一种可行的方法是基于STSA的概率有限状态自动机(PFSA)，最近的文献表明，它需要的训练数据要少得多，并且在训练和测试方面比NN快得多。这份技术简介报告了对当前PFSA方法的修改，以适应(可能是异质的，但不一定是紧密同步的)多传感器数据融合和(基于监督学习的)模式分类。通过对由强迫Duffing方程的仿真模型生成的位置和速度传感器数据的时间序列的融合，验证了该方法的有效性。
Symbolic time series analysis (STSA) plays an important role in the investigation of continuously evolving dynamical systems, where the capability to interpret the joint effects of multiple sensor signals is essential for adequate representation of the embedded knowledge. This technical brief develops and validates, by simulation, an STSA-based algorithm to make timely decisions on dynamical systems for information fusion and pattern classification from ensembles of multisensor time series data. In this context, one of the most commonly used methods has been neural networks (NN) in their various configurations; however, these NN-based methods may require large-volume data and prolonged computational time for training. An alternative feasible method is the STSA-based probabilistic finite state automata (PFSA), which has been shown in recent literature to require significantly less training data and to be much faster than NN for training and, to some extent, for testing. This technical brief reports a modification of the current PFSA methods to accommodate (possibly heterogeneous and not necessarily tightly synchronized) multisensor data fusion and (supervised learning-based) pattern classification in real-time. Efficacy of the proposed method is demonstrated by fusion of time series of position and velocity sensor data, generated from a simulation model of the forced Duffing equation.