A chaotic prediction algorithm using a new cost function

DOI

10.2991/iccsee.2013.126How to use a DOI?

Keywords

Chaotic time series, density function, cost function, negentropy

Abstract

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.

Copyright

© 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/).

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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  -