Mahdaviani, K, Mazyar, H, Majidi, S and Saraee, M 2008, A method to resolve the overfitting problem in recurrent neural networks for prediction of complex system's behavior , in: IEEE World Congress on Computational Intelligence / IEEE International Joint Conference on Neural Networks, 1-6 June 2008, Hong Kong, China.
- Published Version
Restricted to Repository staff only
Download (317kB) | Request a copy
In this paper a new method to resolve the overfitting problem for predicting complex systems' behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks. Finally the results of using this method on two sample datasets are presented and the effectiveness of this method is illustrated.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Print ISBN: 978-1-4244-1820-6|
|Themes:||Media, Digital Technology and the Creative Economy
Memory, Text and Place
|Schools:||Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)|
|Journal or Publication Title:||Proceedings of IEEE World Congress on Computational Intelligence/IEEE International Joint Conference on Neural Networks|
|Depositing User:||Dr Mo Saraee|
|Date Deposited:||26 Oct 2011 13:48|
|Last Modified:||29 Oct 2015 00:11|
Actions (login required)
|Edit record (repository staff only)|