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Online spatiotemporal modeling for time-varying distributed parameter systems using Kernel-based Multilayer Extreme Learning Machine
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DOI：10.1007/s11071-021-06987-y.png)
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Abstract
En 中文
Many advanced industrial processes are a class of time-varying distributed parameter systems (DPSs). It is not an easy task for traditional spatiotemporal modeling methods to approximate these systems because of the inherent time-varying and strong nonlinear characteristics. To address this problem, a novel online spatiotemporal modeling method using Kernel-based Multilayer Extreme Learning Machine is proposed to model the time-varying DPSs. First, the Kernel-based Multilayer Extreme Learning Machine is designed to create a deep network through stacking multiple Kernel-based Extreme Learning Machine Autoencoders and one original Extreme Learning Machine Autoencoder. In this step, the spatiotemporal output of time-varying DPSs is transformed into low-dimensional time coefficients directly. Then Online Sequential Regularized Extreme Learning Machine is developed to predict temporal dynamics of time-varying DPSs. Finally, based on the temporal dynamics model, Kernel-based Extreme Learning Machine is applied to reconstruct the spatiotemporal dynamics. Simulations on the thermal processes of a lithium-ion battery and a snap curing oven are presented to validate the performance and effectiveness of the proposed modeling method.
Keywords:
Time-varying distributed parameter system
Strong nonlinearity
Kernel-based Multilayer Extreme Learning Machine
Online Spatiotemporal Modeling
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W