Chandra, Rohitash (2013) Adaptive problem decomposition in cooperative coevolution of recurrent networks for time series prediction. [Conference Proceedings]
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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: | https://repository.usp.ac.fj/id/eprint/5686 |
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