Li Xiumin, Zhong Ling, Xue Fangzheng, Zhang Anguo
Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; College of Automation, Chongqing University, Chongqing 400044, China.
PLoS One. 2015 Apr 13;10(4):e0120750. doi: 10.1371/journal.pone.0120750. eCollection 2015.
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.
具有多簇储层拓扑结构的回声状态网络（ESN）在储层计算和鲁棒性方面比具有随机储层拓扑结构的ESN表现更好。然而，这些ESN具有复杂的储层拓扑结构，这导致储层生成困难。本研究聚焦于在有足够先验数据可用的环境中使用ESN时的储层生成问题。因此，提出了一种先验数据驱动的多簇储层生成算法。该算法中的先验数据用于通过计算ESN的精度和标准差来评估储层。储层采用聚类方法生成；只有评估性能更好的储层才能取代前一个储层。当其评估分数达到预设要求时，获得最终的储层。使用Mackey-Glass混沌时间序列进行的预测实验结果表明，所提出的储层生成算法为ESN提供了额外的预测精度，并增加了网络的结构复杂性。进一步的实验还揭示了获得最佳性能时簇数和时间窗口大小的合适值。当ESN获得最大精度时，储层的信息熵达到最大值。
