Title of Article |
Identity activation structural tolerance online sequential circular extreme learning machine for highly dimensional data |
Date of Acceptance |
14 June 2019 |
Journal |
Title of Journal |
Engineering and Applied Science Research (EASR) |
Standard |
SCOPUS |
Institute of Journal |
Faculty of Engineering, Khon Kaen University |
ISBN/ISSN |
ISSN 2539-6161 (Print) ISSN 2539-6218 (Online) |
Volume |
2019 |
Issue |
2 |
Month |
3 |
Year of Publication |
2019 |
Page |
120-129 |
Abstract |
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. |
Keyword |
Extreme learning machine, Circular extreme machine, Online sequential • Quadratic function |
Author |
|
Reviewing Status |
มีผู้ประเมินอิสระ |
Status |
ตีพิมพ์แล้ว |
Level of Publication |
นานาชาติ |
citation |
true |
Part of thesis |
true |
Attach file |
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Citation |
0
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