Recurrence plot is an effective tool for portraying system dynamics. However, dealing with distance matrices through the Heaviside function which is difficult to determine the threshold may lead to the loss of much important information, such as information about transitions between states. Therefore, in this paper, we propose a novel dispersion heterogeneous recurrence analysis for complex systems to explore their intrinsic characteristics and structure. The use of dispersion patterns in symbolic dynamics can retain valuable information better than the original defined recurrence plots and avoid the challenge of choosing a threshold. Moreover, we use the iterated function system to provide a visual display of the transition information between patterns. Finally, attention entropy is used to develop a dispersion heterogeneous recurrence quantification analysis. Experimental results show that the method is able to detect the changes of system characteristics with parameters. Also, it can be combined with clustering and classification algorithms for fault detection of railway vehicle systems.

递归图是描绘系统动力学的有效工具。但是，通过 Heaviside 函数处理距离矩阵很难确定阈值，可能会导致很多重要信息的丢失，例如状态之间的转换信息。因此，在本文中，我们提出了一种新的复杂系统色散异构递归分析，以探索其内在特征和结构。在符号动力学中使用分散模式可以比原始定义的递归图更好地保留有价值的信息，并避免选择阈值的挑战。此外，我们使用迭代函数系统来提供模式之间转换信息的可视化显示。最后，注意熵用于开发分散异构递归量化分析。实验结果表明，该方法能够检测出系统特性随参数的变化。此外，它还可以与聚类和分类算法相结合，用于铁路车辆系统的故障检测。