Multilayer Online Sequential Reduced Kernel Extreme Learning Machine-Based Modeling for Time-Varying Distributed Parameter Systems
针对时变分布参数系统，提出一种多层在线序贯约减核极限学习机方法，通过深度网络降维和动态时序建模，在锂电池热过程实验中验证了其捕捉时变动态的有效性。
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.