A chaotic prediction algorithm using a new cost function
Yun Bu, Wan Xin Kang
Available Online March 2013.
- 10.2991/iccsee.2013.126How to use a DOI?
- Chaotic time series, density function, cost function, negentropy
The traditional cost function, minimization mean square prediction error is not a proper cost function in chaotic series prediction, for many chaotic signals are non-Gaussian distributions. Then we present using minimization error negentropy as new cost function, and derive the nonlinear approximation method. In simulation, the algorithm shows an enhanced performance to a common two order Volterra prediction.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yun Bu AU - Wan Xin Kang PY - 2013/03 DA - 2013/03 TI - A chaotic prediction algorithm using a new cost function BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 494 EP - 496 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.126 DO - 10.2991/iccsee.2013.126 ID - Bu2013/03 ER -