Identity activation structural tolerance online sequential circular extreme learning machine for highly dimensional data

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Sarutte Atsawaraungsuk
Tatpong Katanyukul
Pattarawit Polpinit


The Structural Tolerance Online Sequential Circular Extreme Learning Machine (STOS-CELM) was developed based on the Circular Extreme Learning Machine (CELM) to allow sequential learning and to mitigate the criticality of deciding the number of hidden nodes with the Householder Block exact inverse QRD Recursive Least Squares (HBQRD-RLS) algorithm. A previous study showed significant efficiency improvement using STOS-CELM with sine activation. However, sine activation is periodic. Its periodicity repeatedly maps multiple values of its input to the same output values. Within the context of STOS-CELM, input of the activation is a non-negative real value corresponding to the closeness of the data to CELM kernels. Mapping this non-negative real value to a limited range with a periodic nature causes loss of inherent information. That could restrain the STOS-CELM from reaching its full potential. This article proposes an Identity Activation Structural Tolerance Online Sequential Circular Extreme Learning Machine (ISTOS-CELM) to improve the STOS-CELM by removing the sine function to relieve this issue. Our experimental results show that ISTOS-CELM provides significantly higher accuracy than STOS‑ELM and the original STOS-CELM, while retaining a comparable processing time and robustness to STOS-CELM.


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Atsawaraungsuk, S., Katanyukul, T., & Polpinit, P. (2019). Identity activation structural tolerance online sequential circular extreme learning machine for highly dimensional data. Engineering and Applied Science Research, 46(2), 120-129. Retrieved from


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