Sequential extreme learning machine incorporating survival error potential

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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)194-204
Number of pages11
JournalNeurocomputing
Volume155
DOIs
Publication statusPublished - 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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