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.
Bibliographical noteFunding 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 ).
© 2014 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence