Forecast of Aging of PEMFCs Based on CEEMD-VMD and Triple Echo State Network [PDF]
SensorsAccurately forecasting the degradation trajectory of proton exchange membrane fuel cells (PEMFCs) across a spectrum of operational scenarios is indispensable for effective maintenance scheduling and robust health surveillance.
Jie Sun +9 more
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SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques [PDF]
Scientific ReportsIn the present study, five reservoir computing models are compared and analyzed for bearing fault classification and severity level identification. Three Gramian Angular Field (GAF) methodologies, such as Gramian Angular Summation Field (GASF), Gramian ...
Anjil Shah +4 more
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Impact of time-history terms on reservoir dynamics and prediction accuracy in echo state networks [PDF]
Scientific ReportsThe echo state network (ESN) is an excellent machine learning model for processing time-series data. This model, utilising the response of a recurrent neural network, called a reservoir, to input signals, achieves high training efficiency.
Yudai Ebato +6 more
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Optimizing concrete crack detection an echo state network approach with improved fish migration optimization [PDF]
Scientific ReportsThere are numerous reasons for concrete buildings cracks, like stress loads, material flaws, and environmental impacts. It is important to find and investigate the concrete cracks during analyzing the safety and structural soundness of buildings, bridges,
Zhichun Fang +3 more
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Multi-scale dynamics by adjusting the leaking rate to enhance the performance of deep echo state networks [PDF]
Frontiers in Artificial IntelligenceIntroductionThe deep echo state network (Deep-ESN) architecture, which comprises a multi-layered reservoir layer, exhibits superior performance compared to conventional echo state networks (ESNs) owing to the divergent layer-specific time-scale responses
Shuichi Inoue +9 more
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TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in real-world applications. For more accurate prediction, methods had better grasp more data characteristics. Different from ordinary time series, ISTS is characterized by irregular time intervals of intra-series and different sampling rates of inter-series. However, existing
C. P. Sun +6 more
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Automatic-differentiated Physics-Informed Echo State Network (API-ESN) [PDF]
, 2021 7 pages, 3 ...
Racca A., Magri L.
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Multiple-Reservoir Hierarchical Echo State Network
Mathematics, 2023 Leaky Integrator Echo State Network (Leaky-ESN) is a useful training method for handling time series prediction problems. However, the singular coupling of all neurons in the reservoir makes Leaky-ESN less effective for sophisticated learning tasks.
Shuxian Lun +3 more
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An Adaptive Algorithm of Input Scale for Deep Echo State Networks [PDF]
Jisuanji gongcheng, 2022 Deep Echo State Networks(DESN) is a combination of Echo State Networks(ESN) and the idea of deep learning.A reasonable selection of internal state matrices and weak integration parameters with different spectral radius can effectively enhance the multi ...
LIU Peng, YE Run, YAN Bin, XIE Qian, LIU Rui
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Research on SOC evaluation method and simulation of lithiumbattery based on echo state network
Dianzi Jishu Yingyong, 2023 Taking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of
Du Guangbo +4 more
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