USP Electronic Research Repository

Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction

Chandra, Rohitash (2013) Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. [Conference Proceedings]

[img] PDF - Published Version
Available under License Creative Commons Attribution No Derivatives.

Download (215kB)

Abstract

Cooperative coevolution employs different problem decomposition methods to decompose the neural network problem into subcomponents. The efficiency of a problem decomposition method is dependent on the neural network architecture and the nature of the training problem. The adaptation of problem decomposition methods have been recently proposed which showed that different problem decomposition methods are needed at different phases in the evolutionary process. This paper employs an adaptive cooperative coevolution problem decomposition framework for training recurrent neural networks on chaotic time series problems. The Mackey Glass, Lorenz and Sunspot chaotic time series are used. The results show improvement in performance when compared to cooperative coevolution and other methods from literature.

Item Type: Conference Proceedings
Additional Information: DOI: 10.1109/IJCNN.2013.6706997
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Rohitash Chandra
Date Deposited: 23 Jan 2014 23:33
Last Modified: 14 Jun 2016 03:10
URI: http://repository.usp.ac.fj/id/eprint/5686
UNSPECIFIED

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...