Sequential extreme learning machine incorporating survival error potential

Lei Sun; Badong Chen; Kar Ann Toh; Zhiping Lin

doi:10.1016/j.neucom.2014.12.029

Sequential extreme learning machine incorporating survival error potential

Lei Sun, Badong Chen, Kar Ann Toh, Zhiping Lin

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.

Original language	English
Pages (from-to)	194-204
Number of pages	11
Journal	Neurocomputing
Volume	155
DOIs	https://doi.org/10.1016/j.neucom.2014.12.029
Publication status	Published - 2015 May 1

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A2042428 ). This work was partly supported by Beijing Institute of Technology Basic Science Research Program ( BIT-20130542011 ) funded by BIT, China and by National Natural Science Foundation of China (No. 61372152 ).

Publisher Copyright:
© 2014 Elsevier B.V.

All Science Journal Classification (ASJC) codes

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2014.12.029

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title = "Sequential extreme learning machine incorporating survival error potential",

abstract = "A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.",

author = "Lei Sun and Badong Chen and Toh, {Kar Ann} and Zhiping Lin",

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doi = "10.1016/j.neucom.2014.12.029",

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AU - Sun, Lei

AU - Chen, Badong

AU - Toh, Kar Ann

AU - Lin, Zhiping

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A2042428 ). This work was partly supported by Beijing Institute of Technology Basic Science Research Program ( BIT-20130542011 ) funded by BIT, China and by National Natural Science Foundation of China (No. 61372152 ). Publisher Copyright: © 2014 Elsevier B.V.

PY - 2015/5/1

Y1 - 2015/5/1

N2 - A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.

AB - A sequential extreme learning machine incorporating a noise compensation scheme via an information measure is developed. In this design, the computationally simple extreme learning machine architecture is maintained while survival error information potential function provides a mechanism for noise compensation. The error compensation is updated online via an error codebook design where an error tolerant and stable solution is obtained. The developed method is tested on chaotic time sequence as well as benchmark data sets. Experimental results show potential applications for the developed method.

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ER -

Sequential extreme learning machine incorporating survival error potential

Abstract

Bibliographical note

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