“…To fast train SLFN, Huang et al proposed a learning algorithm called "Extreme Learning Machine" (ELM), which randomly assigns the hidden nodes parameters and then determines the output weights by the Moore-Penrose generalized inverse [4][5][6]. ELM has been successfully applied to many real-world applications, such as retinal vessel segmentation [7], wind speed forecasting [8,9], water network management [10], path-tracking of autonomous mobile robot [11], modelling of drying processes [12], bearing fault diagnosis [13], cybersecurity defense framework [14], crop classification [15], and energy disaggregation [16]. In recent years, ELM has been extended to multilayer ELMs, which play an important role in the deep learning domain [17][18][19][20][21][22][23].…”