.png){kind=icon}
.png){kind=icon}
Return
Improved simple deterministically constructed Cycle Reservoir Network with Sensitive Iterative Pruning Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2014.05.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Reservoir Computing (RC) is an effective approach to design and train recurrent neural networks, which is successfully and widely applied in real-valued time series modeling tasks. However, RC has been criticized for not being principled enough, namely the reservoir which is unlikely to be optimal because the reservoir connectivity and weight structure are created randomly. A new Simple Cycle Reservoir Network (SCRN) with deterministically constructed connectivity and weight structure can yield performance competitive with standard Echo State Network (ESN). In order to determine the proper size of the reservoir and improve generalization ability of SCRN, a Sensitive Iterated Pruning Algorithm (SIPA), in which a larger than necessary reservoir is employed firstly and then its size is reduced by pruning out the least sensitive internal units, is proposed to optimize the reservoir size and weights of SCRN. A system identification and two time-series benchmark tasks are applied to demonstrate the feasibility and superiority of SIPA. The results show that the SIPA method significantly outperforms a Least Angle Regression (LAR) method and SIPA is able to improve the generalization performance of SCRN. Besides, two well known reservoir characterizations, i.e. pseudo-Lyapunov exponent of the reservoir dynamics and Memory Capacity, and the impact of SIPA on two characterizations are investigated. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Echo State Network
Reservoir Computing
Sensitive Iterated Pruning Algorithm
Time-series prediction
Simple Cycle Reservoir Network
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
Organization
No organization information available