期刊
NEUROCOMPUTING
卷 159, 期 -, 页码 58-66出版社
ELSEVIER
DOI: 10.1016/j.neucom.2015.02.029
关键词
Echo state network; Double activation function; Batch gradient descent; State update equation; Time-series prediction
资金
- National Nature Science Foundation of PR China [60974071]
- Program for New Century Excellent Talents in University [NCET-11-1005]
- Nature Science Foundation of Liaoning Province [2014020143]
- First Batch of Science and Technology Projects of Liaoning Province [2011402001]
- Liaoning BaiQianWan Talents Program [2012921061]
In this paper, an improved leaky integrator echo state network is proposed. The improved model, named double activation functions echo state network (DAF-ESN), introduces double activation functions to replace the original single activation function in the reservoir state update equation. For the purely input-driven applications, the model can flexibly modify the reservoir state according to different input signals. A sufficient condition for DAF-ESN is given to guarantee that DAF-ESN has the echo state property. Since double activation functions are represented as the weighted sum, two new parameters are introduced. The batch gradient descent method is utilized to optimize the parameters. Finally, the proposed method is applied to the time-series prediction. Simulation results show that the DAF-ESN model can greatly improve the accuracy of time-series prediction, while the operating time is not increased. (C) 2015 Elsevier B.V. All rights reserved.
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