Zhu Chengjiu, Yang Haidong, Jin Xi, Xu Kangkang, Li Hongcheng
IEEE Trans Cybern. 2024 Jan;54(1):624-634. doi: 10.1109/TCYB.2023.3293196. Epub 2023 Dec 20.
A significant number of industrial dynamic processes belong to time-varying distributed parameter systems (DPSs). To develop an accurate approximation model for these systems, it is critical to capture their time-varying behavior and strong nonlinearity. In this article, a multilayer online sequential reduced kernel extreme learning machine (ML-OSRKELM)-based online spatiotemporal modeling approach is developed for such DPSs. First, ML-OSRKELM stacks multiple online sequential reduced kernel extreme learning machine autoencoders (OSRKELM-AEs) to create a deep network, which can translate the spatiotemporal domain into a low-dimensional time domain. Then, an online sequential reduced kernel extreme learning machine (OS-RKELM) is employed to construct a dynamic temporal model. Finally, after obtaining time coefficients from the time domain, OS-RKELM is also used to reconstruct the original spatiotemporal domain. By using the kernel trick and the support vector selection strategy, the proposed method can remove redundant information while maintaining satisfactory nonlinear learning performance. Furthermore, the designed sequential update scheme can update the model parameters with real-time data, which makes it a promising method for capturing time-varying dynamics. Experiments and simulations on a lithium-ion battery's thermal process confirm the excellent performance and validity of the proposed model.
大量的工业动态过程属于时变分布参数系统（DPSs）。为这些系统开发一个精确的近似模型，关键是捕捉它们的时变行为和强非线性。在本文中，针对此类DPSs，开发了一种基于多层在线序列约简核极限学习机（ML-OSRKELM）的在线时空建模方法。首先，ML-OSRKELM堆叠多个在线序列约简核极限学习机自动编码器（OSRKELM-AEs）以创建一个深度网络，该网络可以将时空域转换为低维时域。然后，使用在线序列约简核极限学习机（OS-RKELM）构建一个动态时间模型。最后，在从时域获得时间系数后，OS-RKELM还用于重建原始时空域。通过使用核技巧和支持向量选择策略，该方法可以去除冗余信息，同时保持令人满意的非线性学习性能。此外，所设计的序列更新方案可以利用实时数据更新模型参数，这使其成为捕捉时变动态的一种有前途的方法。对锂离子电池热过程的实验和仿真证实了所提模型的优异性能和有效性。
