Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6865985 | Neurocomputing | 2015 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin,