Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems

By Hai-Jun Rong, G. Huang, Narasimhan Sundararajan, Paramasivan Saratchandran. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009

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Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (2020, methodology): “We then use the last six datasets in Table III which are also adopted in [22] to compare some other neuro-fuzzy systems including OS-F-ELM, ANFIS, and Simpl_eTS with our fuzzy BLS.”
Extreme Learning Machine for Regression and Multiclass Classification (2012, background): “ELM [12], [13] and its variants [14]–[16], [24]–[28] mainly focus on the regression applications.”
Trends in extreme learning machines: A review (2015, methodology): “We also witness the extensions of ELM for online sequential data (Lan, Soh, & Huang, 2009; Liang, Huang, Saratchandran, & Sundararajan, 2006; Rong, Huang, Sundararajan, & Saratchandran, 2009; Ye, Squartini, & Piazza, 2013; Zhao, Wang, & Park, 2012), noisy/missing data (Horata, Chiewchanwattana, &…”
Extreme learning machine: algorithm, theory and applications (2013, background): “Rong et al. (2008) address the architectural design of the ELM classifier network, since too few/many hidden nodes employedwould lead to under-fitting/over-fitting issues in pattern classification.”