油藏计算
回声状态网络
计算机科学
Echo(通信协议)
国家(计算机科学)
系列(地层学)
状态空间
领域(数学)
算法
循环神经网络
理论计算机科学
人工智能
数学
人工神经网络
地质学
计算机网络
古生物学
统计
纯数学
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:2011-01-01
卷期号:22 (1): 131-144
被引量:530
标识
DOI:10.1109/tnn.2010.2089641
摘要
Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) $MC$ of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.
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