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Journal articles on the topic 'Deep Echo State Networks'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Gallicchio, Claudio, Alessio Micheli, and Luca Pedrelli. "Design of deep echo state networks." Neural Networks 108 (December 2018): 33–47. http://dx.doi.org/10.1016/j.neunet.2018.08.002.

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2

Gallicchio, Claudio, Alessio Micheli, and Luca Silvestri. "Local Lyapunov exponents of deep echo state networks." Neurocomputing 298 (July 2018): 34–45. http://dx.doi.org/10.1016/j.neucom.2017.11.073.

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3

Gallicchio, Claudio, and Alessio Micheli. "Echo State Property of Deep Reservoir Computing Networks." Cognitive Computation 9, no. 3 (2017): 337–50. http://dx.doi.org/10.1007/s12559-017-9461-9.

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4

Kim, Taehwan, and Brian R. King. "Time series prediction using deep echo state networks." Neural Computing and Applications 32, no. 23 (2020): 17769–87. http://dx.doi.org/10.1007/s00521-020-04948-x.

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5

Long, Jianyu, Shaohui Zhang, and Chuan Li. "Evolving Deep Echo State Networks for Intelligent Fault Diagnosis." IEEE Transactions on Industrial Informatics 16, no. 7 (2020): 4928–37. http://dx.doi.org/10.1109/tii.2019.2938884.

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6

Pecorella, Tommaso, Romano Fantacci, and Benedetta Picano. "Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks." Sensors 20, no. 22 (2020): 6475. http://dx.doi.org/10.3390/s20226475.

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The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, th

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7

Ma, Qianli, Lifeng Shen, and Garrison W. Cottrell. "DeePr-ESN: A deep projection-encoding echo-state network." Information Sciences 511 (February 2020): 152–71. http://dx.doi.org/10.1016/j.ins.2019.09.049.

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8

Song, Zuohua, Keyu Wu, and Jie Shao. "Destination prediction using deep echo state network." Neurocomputing 406 (September 2020): 343–53. http://dx.doi.org/10.1016/j.neucom.2019.09.115.

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9

McDermott, Patrick L., and Christopher K. Wikle. "Deep echo state networks with uncertainty quantification for spatio‐temporal forecasting." Environmetrics 30, no. 3 (2018): e2553. http://dx.doi.org/10.1002/env.2553.

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10

Guo, Xiang, Ruiqi Liang, Shule Xu, Linyu Dong, and Yang Liu. "An investigation of echo state network for EEG-based emotion recognition with deep neural networks." Biomedical Signal Processing and Control 111 (January 2026): 108342. https://doi.org/10.1016/j.bspc.2025.108342.

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11

Shen, Qingyu, Hanwen Zhang, and Yao Mao. "Improving Deep Echo State Network with Neuronal Similarity-Based Iterative Pruning Merging Algorithm." Applied Sciences 13, no. 5 (2023): 2918. http://dx.doi.org/10.3390/app13052918.

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Recently, a layer-stacked ESN model named deep echo state network (DeepESN) has been established. As an interactional model of a recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal si

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12

Soltani, Rebh, Emna Benmohamed, and Hela Ltifi. "Newman-Watts-Strogatz topology in deep echo state networks for speech emotion recognition." Engineering Applications of Artificial Intelligence 133 (July 2024): 108293. http://dx.doi.org/10.1016/j.engappai.2024.108293.

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13

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. (10) (2023): 1116–38. https://doi.org/10.3897/jucs.98789.

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Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla

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14

Xu, Shiyun, Changjun He, Bosong Yan, and Mingjiang Wang. "A Multi-Stage Acoustic Echo Cancellation Model Based on Adaptive Filters and Deep Neural Networks." Electronics 12, no. 15 (2023): 3258. http://dx.doi.org/10.3390/electronics12153258.

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The presence of a large amount of echoes significantly impairs the quality and intelligibility of speech during communication. To address this issue, numerous studies and models have been conducted to cancel echo. In this study, we propose a multi-stage acoustic echo cancellation model that utilizes an adaptive filter and a deep neural network. Our model consists of two parts: the Speex algorithm for canceling linear echo, and the multi-scale time-frequency UNet (MSTFUNet) for further echo cancellation. The Speex algorithm takes the far-end reference speech and the near-end microphone signal a

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15

Bonci, Andrea, Lorenzo Longarini, Sauro Longhi, Geremia Pompei, and Mariorosario Prist. "Process regulation control using Echo State Networks: an ESN-based deep neural network approach for PID control." Procedia Computer Science 253 (2025): 2369–76. https://doi.org/10.1016/j.procs.2025.01.297.

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16

Li, Xin, Fengrong Bi, Lipeng Zhang, Xiao Yang, and Guichang Zhang. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer." Energies 15, no. 3 (2022): 1205. http://dx.doi.org/10.3390/en15031205.

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This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MV

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17

D, Cenitta, VIJAYA ARJUNAN RANGANATHAN, Tanuja Shailesh, Andrew J, Arul N, and Praveen Pai T. "Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction." F1000Research 14 (July 3, 2025): 650. https://doi.org/10.12688/f1000research.165575.1.

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Background Early and accurate prediction of ischemic heart disease (IHD) is essential for reducing mortality and enabling timely intervention. Misdiagnosis can lead to severe health outcomes, emphasizing the need for robust and intelligent predictive models. Deep learning approaches have shown strong potential in identifying hidden patterns in medical data and aiding clinical decision-making. Methods This study proposes a novel Hybrid Residual Attention with Echo State Network (HRAESN) model that integrates Attention Residual Learning (ARL) with Echo State Networks (ESN) to enhance feature ext

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18

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. 10 (2023): 1116–38. http://dx.doi.org/10.3897/jucs.98789.

Full text

Abstract:

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla

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19

Wang, Kexin, Yihong Gao, Mauro Dragone, Yvan Petillot, and Xu Wang. "A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals." Photonics 10, no. 7 (2023): 763. http://dx.doi.org/10.3390/photonics10070763.

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Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signa

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20

Liu, Ronghui, and Junmin Zhao. "A New Deep Neural Network Based on Multi-Layer Echo State Network." Recent Patents on Computer Science 11, no. 1 (2018): 44–51. http://dx.doi.org/10.2174/2213275911666180507112411.

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21

Bo, Ying-Chun, Ping Wang, Xin Zhang, and Bao Liu. "Modeling data-driven sensor with a novel deep echo state network." Chemometrics and Intelligent Laboratory Systems 206 (November 2020): 104062. http://dx.doi.org/10.1016/j.chemolab.2020.104062.

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22

Gao, Ruobin, Ruilin Li, Minghui Hu, Ponnuthurai Nagaratnam Suganthan, and Kum Fai Yuen. "Dynamic ensemble deep echo state network for significant wave height forecasting." Applied Energy 329 (January 2023): 120261. http://dx.doi.org/10.1016/j.apenergy.2022.120261.

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23

Xu, Yuebing, Jing Zhang, Zuqiang Long, Hongzhong Tang, and Xiaogang Zhang. "Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network." Water 11, no. 2 (2019): 351. http://dx.doi.org/10.3390/w11020351.

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Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the prediction of hourly urban water demand. The CDBESN model uses a continuous deep belief network (CDBN) as the feature extraction algorithm and an echo state network (ESN) as the regression algorithm. The new architecture can model actual water demand data with fast convergence and global optimization ability. The prediction capacity of the CDBES

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24

Cheng, Xu, and Chenyuan Zhao. "Prediction of Tourist Flow Based on Deep Belief Network and Echo State Network." Revue d'Intelligence Artificielle 33, no. 4 (2019): 275–81. http://dx.doi.org/10.18280/ria.330403.

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25

Li, Qian, Tao Li, Jiangang Ouyang, Dayong Yang, and Zhijun Guo. "Deep Echo State Network with Variable Memory Pattern for Solar Irradiance Prediction." Complexity 2022 (October 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/8506312.

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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. As a new kind of recurrent neural network, echo state network (ESN) shows excellent performance in the field of time series prediction. However, the memory length of classical ESN is fixed and finite, which makes it hard to map sufficient features of solar irradiance with long-range dependency. Therefore, a novel deep echo state network with variable memory pattern (VMP-DESN) is proposed in this bri

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26

Wang, Zhanshan, Xianshuang Yao, Zhanjun Huang, and Lei Liu. "Deep Echo State Network With Multiple Adaptive Reservoirs for Time Series Prediction." IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2021): 693–704. http://dx.doi.org/10.1109/tcds.2021.3062177.

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27

Zhang, Huiyan, Bo Hu, Xiaoyi Wang, et al. "Self-organizing deep belief modular echo state network for time series prediction." Knowledge-Based Systems 222 (June 2021): 107007. http://dx.doi.org/10.1016/j.knosys.2021.107007.

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28

Ma, Gaohong, and Jun Li. "Short-term Global Horizontal Irradiance Prediction Based on Deep Echo State Network." Journal of Physics: Conference Series 2171, no. 1 (2022): 012028. http://dx.doi.org/10.1088/1742-6596/2171/1/012028.

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Abstract The prediction of global horizontal irradiance have a great impact on the stability and economic benefits of photovoltaic (PV) power generation. In this paper, we adopt the method of Deep Echo State Network (DESN) to predict the global horizontal irradiance in different areas one hour in advance. Under the same conditions, the results show that DESN are better than BP, SVM, ESN methods in the prediction accuracy. Experiments show that the proposed models show superior ability in predicting solar irradiance and have great application potential in power grid integration.

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29

Sun, Xiaochuan, Tao Li, Qun Li, Yue Huang, and Yingqi Li. "Deep belief echo-state network and its application to time series prediction." Knowledge-Based Systems 130 (August 2017): 17–29. http://dx.doi.org/10.1016/j.knosys.2017.05.022.

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30

Hu, Huanling, Lin Wang, and Sheng-Xiang Lv. "Forecasting energy consumption and wind power generation using deep echo state network." Renewable Energy 154 (July 2020): 598–613. http://dx.doi.org/10.1016/j.renene.2020.03.042.

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31

Pontes-Filho, Sidney, Pedro Lind, Anis Yazidi, et al. "A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality." Cognitive Neurodynamics 14, no. 5 (2020): 657–74. http://dx.doi.org/10.1007/s11571-020-09600-x.

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Abstract Although deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, we investigate an alternative brain-inspired method for data analysis that circumvents the deep learning drawbacks by taking the actual dynamical behavior of biological neural networks into account. For this purpose, we develop a general framework for dynamical systems that can evolve and model a variety of substrates that possess computational capacity. Therefore, dyn

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32

Li, Zhigang, Jialin Wang, Difei Cao, et al. "Investigating Neural Activation Effects on Deep Belief Echo-State Networks for Prediction Toward Smart Ocean Environment Monitoring." Arabian Journal for Science and Engineering 46, no. 4 (2021): 3913–23. http://dx.doi.org/10.1007/s13369-020-05319-3.

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33

Bai, Kangjun, Yang Yi, Zhou Zhou, Shashank Jere, and Lingjia Liu. "Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10, no. 2 (2020): 253–63. http://dx.doi.org/10.1109/jetcas.2020.2992238.

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34

Wang, Qiang, Linqing Wang, Ying Liu, Jun Zhao, and Wei Wang. "Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network." IEEE Access 7 (2019): 152533–44. http://dx.doi.org/10.1109/access.2019.2948367.

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35

Bai, Yu-Ting, Wei Jia, Xue-Bo Jin, Ting-Li Su, Jian-Lei Kong, and Zhi-Gang Shi. "Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization." Mathematics 11, no. 6 (2023): 1503. http://dx.doi.org/10.3390/math11061503.

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The predictions from time series data can help us sense development trends and make scientific decisions in advance. The commonly used forecasting methods with backpropagation consume a lot of computational resources. The deep echo state network (DeepESN) is an advanced prediction method with a deep neural network structure and training algorithm without backpropagation. In this paper, a Bayesian optimization algorithm (BOA) is proposed to optimize DeepESN to address the problem of increasing parameter scale. Firstly, the DeepESN was studied and constructed as the basic prediction model for th

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36

Bhandage, Venkatesh, Manjunath G, Nijaguna Gollara Siddappa, et al. "HRAESN-IoT: A Hybrid Residual Attention and Echo State Network Approach for IoT-Enabled Heart Disease Prediction." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 375–89. https://doi.org/10.58346/jowua.2025.i1.023.

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Detection of Ischemic Heart Disease needs immediate accurate identifications since incorrect medical assessments lead to serious outcomes. A perfect heart disease prediction model must combine deep learning techniques with the Internet of Things (IoT) for achieving diagnoses of high accuracy. The authors present HRAESN-IoT as a real-time IHD severity prediction method that retrieves patient data from wearable sensors enabled by Internet of Things technology. Using attention residual learning together with Echo State Network (ESN) the model discovers significant medical patterns along with main

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37

Sun, Weitong, Yuping Su, Xia Wu, Xiaojun Wu, and Yumei Zhang. "EEG denoising through a wide and deep echo state network optimized by UPSO algorithm." Applied Soft Computing 105 (July 2021): 107149. http://dx.doi.org/10.1016/j.asoc.2021.107149.

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38

Song, Xuefei, and Zhong Shuo Chen. "Shipping market time series forecasting via an Ensemble Deep Dual-Projection Echo State Network." Computers and Electrical Engineering 117 (July 2024): 109218. http://dx.doi.org/10.1016/j.compeleceng.2024.109218.

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39

Chen, Mingzhe, Walid Saad, and Changchuan Yin. "Echo-Liquid State Deep Learning for 360° Content Transmission and Caching in Wireless VR Networks With Cellular-Connected UAVs." IEEE Transactions on Communications 67, no. 9 (2019): 6386–400. http://dx.doi.org/10.1109/tcomm.2019.2917440.

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40

López-Ortiz, E. J., M. Perea-Trigo, L. M. Soria-Morillo, J. A. Álvarez-García, and J. J. Vegas-Olmos. "Energy-Efficient Edge and Cloud Image Classification with Multi-Reservoir Echo State Network and Data Processing Units." Sensors 24, no. 11 (2024): 3640. http://dx.doi.org/10.3390/s24113640.

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In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational

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41

Wang, Heshan, Q. M. Jonathan Wu, Jianbin Xin, Jie Wang, and Heng Zhang. "Optimizing Deep Belief Echo State Network with a Sensitivity Analysis Input Scaling Auto-Encoder algorithm." Knowledge-Based Systems 191 (March 2020): 105257. http://dx.doi.org/10.1016/j.knosys.2019.105257.

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42

Pei, Yanle, Qian Li, Yayi Wu, et al. "MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation." Remote Sensing 16, no. 19 (2024): 3597. http://dx.doi.org/10.3390/rs16193597.

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Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network

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43

Yuan, Yi, and DongXia Zheng. "Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation." Mobile Information Systems 2022 (August 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/7581079.

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With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a

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44

Sheng, Hui, Min Liu, Jiyong Hu, Ping Li, Yali Peng, and Yugen Yi. "LA-ESN: A Novel Method for Time Series Classification." Information 14, no. 2 (2023): 67. http://dx.doi.org/10.3390/info14020067.

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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 have become mainstream. Echo State Networks (ESN) and Convolutional Neural Networks (CNN) are commonly utilized as deep neural network methods in TSC research. However, ESN and CNN can only extract local dependencies relations of time series, resulting in long-term temporal data dependence needing to be more challenging to capture. As a result, a

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45

杉本, 桂太郎. "Time Series Prediction of Machine Tool Sensor Data with Deep Echo State Network for Anomaly Detection." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2021 (2021): IIP1A1–2. http://dx.doi.org/10.1299/jsmeiip.2021.iip1a1-2.

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46

V, Sujatha Bai, and Punithavalli M. "Evolutionary Optimization Algorithm with Deep Echo State Network for Anomaly Detection on Secure Cloud Computing Environment." International Journal of Electrical and Electronics Engineering 10, no. 4 (2023): 46–56. http://dx.doi.org/10.14445/23488379/ijeee-v10i4p105.

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47

Jebur, Tuka Kareem. "Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink." Periodicals of Engineering and Natural Sciences (PEN) 9, no. 1 (2021): 48. http://dx.doi.org/10.21533/pen.v9i1.1769.

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48

Serrano, Will. "Deep echo state networks in data marketplaces." Machine Learning with Applications, February 2023, 100456. http://dx.doi.org/10.1016/j.mlwa.2023.100456.

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49

Zhang, Shaohui, Zhenzhong Sun, Man Wang, Jianyu Long, Yun Bai, and Chuan Li. "Deep Fuzzy Echo State Networks for Machinery Fault Diagnosis." IEEE Transactions on Fuzzy Systems, 2019, 1. http://dx.doi.org/10.1109/tfuzz.2019.2914617.

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50

Shen, Qingyu, Junzhe Wang, Hanwen Zhang, Jinjin Peng, Minxing Sun, and Yao Mao. "Growing evolutional deep echo state network." Neurocomputing, October 2024, 128676. http://dx.doi.org/10.1016/j.neucom.2024.128676.

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