A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
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一种先验数据驱动的 Echo 状态网络多集群储层生成算法
DOI：
10.1371/journal.pone.0120750
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhang A
中科院分区：
文献类型：
--
作者：
Li X;Zhong L;Xue F;Zhang A
具有多簇水库拓扑的回声状态网络在水库计算和鲁棒性方面优于具有随机水库拓扑的回声状态网络。然而，这些ESN具有复杂的储层拓扑结构，这导致储层生成的困难。本研究的重点是当ESN是在环境中有足够的先验数据可用的油藏生成问题。在此基础上，提出了一种先验数据驱动的多聚类油藏生成算法。该算法利用先验数据，通过计算ESN的精度和标准差，对储层进行评价。利用聚类方法对储层进行划分，只有评价性能较好的储层才能代替前一个储层。当储层的评价得分达到预定要求时，得到最终储层。对Mackey-Glass混沌时间序列的预测实验结果表明，所提出的水库生成算法不仅提高了ESN的预测精度，而且增加了网络的结构复杂度。进一步的实验也揭示了适当的值的簇的数量和时间窗口大小，以获得最佳的性能。当ESN获得最大精度时，储层的信息熵达到最大。
Echo state networks (ESNs) with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.
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DOI：
10.1073/pnas.0806082105
发表时间：
2008-12-16
影响因子：
11.1
作者：
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影响因子：
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作者：
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DOI：
10.1016/j.expthermflusci.2014.09.008
发表时间：
2015-01-01
影响因子：
3.2
作者：
Gao, Zhong-Ke;Fang, Peng-Cheng;Jin, Ning-De
通讯作者：
Jin, Ning-De
影响因子：
6
作者：
Gallicchio, Claudio;Micheli, Alessio
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Micheli, Alessio