Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Many industrial thermal processes are large-scale time-varying nonlinear distributed parameter systems (DPSs). To effectively model such systems, dual extreme learning machine based online spatiotemporal modeling with adaptive forgetting factor (AFFD-ELM) is proposed in this paper. This method can recursively update the parameters of the low-order temporal model by using newly arriving data under Karhunen- Loeve (KL) based space/time separation. In this way, the time-varying dynamics can be tracked real-time very well as output data increases over time. Besides, since the training samples are usually timeliness, adaptive forgetting factor (AFF) is also embedded in this method to improve the online learning effects by adding a reasonable weight to previous data. This online learning strategy makes the process promising for online modeling under continuously samples environment. The proposed method is utilized for online temperature prediction of the curing oven. Simulation results verify the efficiency and viability of the online spatiotemporal model.

Automated summary

This paper proposes an online spatiotemporal modeling method based on dual extreme learning machine with adaptive forgetting factor, which can effectively track time-varying dynamics in real time and improve online learning effects.