.png){kind=icon}
.png){kind=icon}
Return
Recurrent neural system with minimum complexity: A deep learning perspective
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.09.075.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel echo state network (ESN) architecture in a deep learning framework for time series prediction. The architecture is a uniform and consistent system with functional parts of the pre-training input network that effectively captures information with different degrees of abstraction in the observed data, and the minimum complexity ESN that possesses the powerful nonlinear approximation capability and highly efficient training. To our best knowledge, this is the first systematic model attempting to introduce the deep learning methodology to the ESN modeling, which provides a more robust alternative to the conventional shallow ESNs. Extensive experiments on various widely used benchmarks of different origins and features show that our model achieves a great enhancement in the prediction accuracy and short-term memory capacity, without significant tradeoff in the model's computational efficiency. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Echo state network
Deep belief network
Time series prediction
Memory capacity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W