Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug-Resistant Epilepsy. [PDF]
Ann Clin Transl NeurolABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong JW +7 more
europepmc +2 more sources
Controller Design Based on Echo State Network with Delay Output for Nonlinear System
Complexity, 2020 For the nonlinear systems with delay output, the control performance of the system is affected by the previous output of the system, such as crawling robots of photovoltaic panels.
Xianshuang Yao +3 more
doaj +1 more source
Tailoring Echo State Networks for Optimal Learning
iScience, 2020 Summary: As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine, finance, and language processing.
Pau Vilimelis Aceituno +2 more
doaj +1 more source
LA-ESN: A Novel Method for Time Series Classification
Information, 2023 Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods ...
Hui Sheng +5 more
doaj +1 more source
Estimation of heat generation by using echo state network for diagnosing thermal characteristics
Measurement: Sensors, 2021 Temperature measurement can be an effective method for diagnosing anomalies in equipment. The amount of internally generated heat may be due to load or an anomaly. Therefore, it is desirable to know how much heat was generated at what time.
Yukio Hiranaka, Koichi Tsujino
doaj +1 more source
Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process [PDF]
, 2012 —In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dy-namics and producing predictive distributions (instead of point predictions ...
Demiris, Y, Soh, H
core +1 more source
Training Echo State Networks with Regularization through Dimensionality Reduction [PDF]
, 2016 In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series. The method consists in projecting the output of the internal layer of the network on a space with lower dimensionality, before training the ...
Bianchi, Filippo Maria +2 more
core +2 more sources
Musical instrument mapping design with Echo State Networks [PDF]
, 2014 Echo State Networks (ESNs), a form of recurrent neural network developed in the field of Reservoir Computing, show significant potential for use as a tool in the design of mappings for digital musical instruments.
Kiefer, Chris
core +2 more sources
Adaptive Levenberg-Marquardt Algorithm Based Echo State Network for Chaotic Time Series Prediction
IEEE Access, 2018 Echo state networks (ESNs) have wide applications in chaotic time series prediction. In the ESN, if the smallest singular value of the reservoir state matrix is infinitesimal, the ill-posed problem might occur during the training process.
Junfei Qiao, Lei Wang, Cuili Yang, Ke Gu
doaj +1 more source
Deep Echo State Network with Variable Memory Pattern for Solar Irradiance Prediction
Complexity, 2022 Accurate solar irradiance prediction plays an important role in ensuring the security and stability of renewable energy systems. Solar irradiance modeling is usually a time-dependent dynamic model.
Qian Li +4 more
doaj +1 more source