Results 21 to 30 of about 317,020 (282)
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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BMC Medical Informatics and Decision Making, 2010 Background Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data.
De Turck F +6 more
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Tree Echo State Networks [PDF]
Neurocomputing, 2013 In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach:
GALLICCHIO, CLAUDIO, MICHELI, ALESSIO
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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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The spectral radius remains a valid indicator of the echo state property for large reservoirs [PDF]
, 2013 In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property.
Caluwaerts, Ken +3 more
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Tailoring Echo State Networks for Optimal Learning [PDF]
SSRN Electronic Journal, 2020 iScience, 23 (9)
Aceituno, Pau Vilimelis +2 more
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Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories [PDF]
, 2020 © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new ...
Ai, Jiaqiu +4 more
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Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network
IEEE Access, 2019 In the complex industrial environment, data missing situation is often occurred in the process of data acquisition and transition. The major contribution of the paper is the proposal of a deep bidirectional echo state network (DBESN) framework for time ...
Qiang Wang +4 more
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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
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Design of deep echo state networks [PDF]
Neural Networks, 2018 In this paper, we provide a novel approach to the architectural design of deep Recurrent Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir Computing framework and inspired by the principles related to the inherent effect of layering, we address a fundamental open issue in deep learning, namely the question of how
Gallicchio, Claudio +2 more
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