• Vol 8, No 4 (2017)
  • Electrical, Electronics, and Computer Engineering

Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Sarutte Atsawaraungsuk, Tatpong Katanyukul


Publish at : 31 Jul 2017 - 00:00
IJtech : IJtech Vol 8, No 4 (2017)
DOI : https://doi.org/10.14716/ijtech.v8i4.9476

Cite this article as:
Atsawaraungsuk, S.., & Katanyukul, T.. 2017. Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine. International Journal of Technology. Volume 8(4), pp.601-610
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Sarutte Atsawaraungsuk Department of Computer Engineering, Faculty of Engineering, Khon Kaen University, 123 Moo 16 Mittapap Rd., Muang, Khon Kaen 40002, Thailand
Tatpong Katanyukul
Email to Corresponding Author

Abstract

This article discusses the development of the online sequential circular extreme learning machine (OS-CELM) and structural tolerance OS-CELM (STOS-CELM). OS-CELM is developed based on the circular extreme learning machine (CELM) to enable sequential learning. It can update a new chunk of data by spending less training time to update the chunk than the batch CELM. STOS-CELM is developed based on an idea similar to that of OS-CELM, but with a Householder block exact inverse QR decomposition (QRD) recursive least squares (QRD-RLS) algorithm to allow sequential learning and mitigate the criticality of deciding the number of hidden nodes. In addition, our experiments have shown that given the same hidden node setting, STOS-CELM can deliver accuracy comparable to a batch CELM approach and also has higher accuracy than the original online sequential extreme learning machine (OS-ELM) and structural tolerance OS-ELM (STOS-ELM) in classification problems, especially those involving high dimension datasets.

Circular extreme learning machine; Extreme learning machine; Householder block exact QRD recursive least squares algorithm; Online sequential extreme learning machine