A priori data-driven multi-clustered reservoir generation algorithm for echo state network

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 res...

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Bibliographic Details
Published in:PloS one 2015-04, Vol.10 (4), p.e0120750-e0120750
Main Authors: Li, Xiumin, Zhong, Ling, Xue, Fangzheng, Zhang, Anguo
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Cluster Analysis
Clustering
Complexity
Computer Simulation
Database Management Systems
Databases, Factual
Entropy (Information theory)
Evaluation
Forecasts and trends
Models, Neurological
Neural Networks (Computer)
Reservoirs
Reservoirs (Water)
Topology
Windows (intervals)
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0120750