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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (9): 689-701    DOI: 10.1631/jzus.C1200069
    
Modeling deterministic echo state network with loop reservoir
Xiao-chuan Sun, Hong-yan Cui, Ren-ping Liu, Jian-ya Chen, Yun-jie Liu
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China; ICT Centre, Commonwealth Scientific and Industrial Research Organization, Sydney 1710, Australia
Modeling deterministic echo state network with loop reservoir
Xiao-chuan Sun, Hong-yan Cui, Ren-ping Liu, Jian-ya Chen, Yun-jie Liu
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China; ICT Centre, Commonwealth Scientific and Industrial Research Organization, Sydney 1710, Australia
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摘要： Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.
关键词： Echo state networks;  Loop reservoir structure;  Memory capacity    
Abstract: Echo state network (ESN), which efficiently models nonlinear dynamic systems, has been proposed as a special form of recurrent neural network. However, most of the proposed ESNs consist of complex reservoir structures, leading to excessive computational cost. Recently, minimum complexity ESNs were proposed and proved to exhibit high performance and low computational cost. In this paper, we propose a simple deterministic ESN with a loop reservoir, i.e., an ESN with an adjacent-feedback loop reservoir. The novel reservoir is constructed by introducing regular adjacent feedback based on the simplest loop reservoir. Only a single free parameter is tuned, which considerably simplifies the ESN construction. The combination of a simplified reservoir and fewer free parameters provides superior prediction performance. In the benchmark datasets and real-world tasks, our scheme obtains higher prediction accuracy with relatively low complexity, compared to the classic ESN and the minimum complexity ESN. Furthermore, we prove that all the linear ESNs with the simplest loop reservoir possess the same memory capacity, arbitrarily converging to the optimal value.
Key words: Echo state networks    Loop reservoir structure    Memory capacity
收稿日期: 2012-03-15 出版日期: 2012-09-05
CLC:  TP183  
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Xiao-chuan Sun
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Jian-ya Chen
Yun-jie Liu

引用本文:

Xiao-chuan Sun, Hong-yan Cui, Ren-ping Liu, Jian-ya Chen, Yun-jie Liu. Modeling deterministic echo state network with loop reservoir. Front. Inform. Technol. Electron. Eng., 2012, 13(9): 689-701.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200069    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I9/689

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