Relevant bibliographies by topics / Deep Echo State Networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep Echo State Networks'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Deep Echo State Networks"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Deep Echo State Networks"

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Song, Hao. "Spectrum Management in Dynamic Spectrum Access: A Deep Reinforcement Learning Approach." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/95953.

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Dynamic spectrum access (DSA) is a promising technology to mitigate spectrum shortage and improve spectrum utilization. However, DSA users have to face two fundamental issues, interference coordination between DSA users and protections to primary users (PUs). These two issues are very challenging, since generally there is no powerful infrastructure in DSA networks to support centralized control. As a result, DSA users have to perform spectrum managements, including spectrum access and power allocations, independently without accurate channel state information. In this thesis, a novel spectrum
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Ibáñez, Soria David 1983. "Analysis of brain dynamics using echo-state networks." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663491.

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In the last decade recurrent neural networks have revolutionized the field of artificial intelligence. Their cyclic connections provide them with memory and thus with the capability of modeling processes with temporal context. Echo-state networks are a framework for recurrent neural networks that enormously simplifies their design and training. In this thesis we explore the capabilities of echo-state networks and their application in EEG feature extraction and classification problems. In a first study, we proved that such networks are capable of detecting generalized synchronization changes be
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Zhou, Zhou. "MIMO-OFDM Symbol Detection via Echo State Networks." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/95945.

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Echo state network (ESN) is a specific neural network structure composed of high dimensional nonlinear dynamics and learned readout weights. This thesis considers applying ESN for symbol detection in multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. A new ESN structure, namely, windowed echo state networks (WESN) is introduced to further improve the symbol detection performance. Theoretical analysis justifies WESN has an enhanced short-term memory (STM) compared with the standard ESN such that WESN can offer better computing ability. Additionally,
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Stuan, Dag. "Evolving Echo State Networks for Minimally Cognitive Unsupervised Learning Tasks." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-22966.

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This thesis investigates the use of Echo State Networks (ESNs) in unsupervised learning environments, byemploying Evolutionary Algorithms (EAs) to evolve ESNs to control an agent that performs a novel, minimally-cognitive learning task. The task employed in this thesis is a modified version of the classic video game Frogger. ESNs are investigated since they promise to combine the temporal abilities seen in other Recurrent Neural Networks (RNNs) with a straightforward method to train the network. However, previous work employing ESNs in unsupervised environments is lacking.The evolved ESNs are
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PAIVA, PAULO ROBERTO MENESES DE. "NEUROEVOLUTIONARY MODELS WITH ECHO STATE NETWORKS APPLIED TO SYSTEM IDENTIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36071@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO<br>BOLSA NOTA 10<br>Através das técnicas utilizadas em Identificação de Sistemas é possível obter um modelo matemático para um sistema dinâmico somente a partir de dados medidos de suas entradas e saídas. Por possuírem comportamento naturalmente dinâmico e um procedimento de treinamento simples e rápido, o uso de redes neurais do tipo Echo State Networks (ESNs) é vantajoso nesta área. Entretanto, as ESNs possuem hiperpar
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Bauduin, Marc. "Equalization of Non-linear Satellite Communication Channels using Echo State Networks." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/239317.

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Satellite communication system designers are continuously struggling to improve the channel capacity. A critical challenge results from the limited power available aboard the satellite.Because of this constraint, the onboard power amplifier must work with a small power supply which limits its maximum output power. To ensure a sufficient Signal-to-Noise power Ratio (SNR) on the receiver side, the power amplifier must work close to its saturation point. This is power efficient but unfortunately adds non-linear distortions to the communication channel. The latters are very penalizing for high ord
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Løvlid, Rikke Amilde. "Internal Models as Echo State Networks : Learning to Execute Arm Movements." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23554.

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As robots are becoming more and more complex, with higher degrees-of-freedom, lighter limbs, and springy joints, it becomes harder to control their movements. New approaches, inspired from neuroscience, are attracting increased attention among computer scientists dealing with motor control. The focus in this thesis is on how robots can learn to control their limbs by learning how their bodies work, i.e., by learning internal models of their motor apparatus. Inspiration from cerebellar research combined with concepts from traditional control theory has been used as a basis. The research in the
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Jirak, Doreen [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Inspection of echo state networks for dynamic gestures / Doreen Jirak ; Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://d-nb.info/113180323X/34.

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Jirak, Doreen Verfasser], and Stefan [Akademischer Betreuer] [Wermter. "Inspection of echo state networks for dynamic gestures / Doreen Jirak ; Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://nbn-resolving.de/urn:nbn:de:gbv:18-84837.

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Wootton, Adam J. "Fusion of heterogeneous data in non-destructive testing and structural health monitoring using Echo State Networks." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5004/.

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Failure to monitor the condition of key infrastructure such as roads and bridges can result in costly closures, but the economic impact could be lessened by early intervention. Non-destructive testing (NDT) examines structures without causing damage, while structural health monitoring (SHM) monitors a structure throughout its life. This thesis presents a machine learning approach to fusing heterogeneous sensor modalities that can be systematically applied to improve sensor interpretation and reduce reliance on expertise. For the first time, echo state networks (ESNs) were used in two separate
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Books on the topic "Deep Echo State Networks"

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Cogan, Susan M. Catholic Social Networks in Early Modern England. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463726948.

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Catholic Social Networks in Early Modern England: Kinship, Gender, and Coexistence explores the lived experience of Catholic women and men in the post-Reformation century. Set against the background of the gendered dynamics of English society, this book demonstrates that English Catholics were potent forces in the shaping of English culture, religious policy, and the emerging nation-state. Drawing on kinship and social relationships rooted in the medieval period, post Reformation English Catholic women and men used kinship, social networks, gendered strategies, political actions, and cultural
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Konrad, Kerstin, Adriana Di Martino, and Yuta Aoki. Brain volumes and intrinsic brain connectivity in ADHD. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198739258.003.0006.

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Neuroimaging studies have increased our understanding of the neurobiological underpinnings of ADHD. Structural brain imaging studies demonstrate widespread changes in brain volumes, in particular in frontal-striatal-cerebellar networks. Based on the widespread nature of structural and functional brain abnormalities, approaches able to capture the organizing principles of large-scale neural systems have been used in ADHD. These include diffusion magnetic resonance imaging (MRI) and resting state functional MRI (R-fMRI). Complementary to findings of volumetric studies, diffusion investigations h
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Richter, Klaus. Fragmentation in East Central Europe. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198843559.001.0001.

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The First World War led to a radical reshaping of Europe’s political borders like hardly any previous event. Nowhere was this transformation more profound than in East Central Europe, where the collapse of imperial rule led to the emergence of a series of new states. New borders intersected centuries-old networks of commercial, cultural, and social exchange. The new states had to face the challenges posed by territorial fragmentation and at the same time establish durable state structures within an international order that viewed them at best as weak and at worst as provisional entities that w
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Arriaga, Eduard, and Andrés Villar, eds. Afro-Latinx Digital Connections. University Press of Florida, 2021. http://dx.doi.org/10.5744/florida/9781683402046.001.0001.

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This volume presents examples of how digital technologies are being used by people of African descent in South America and the Caribbean, a topic that has been overlooked within the field of digital humanities. These case studies show that in the last few decades, Black Latinx communities have been making themselves visible and asserting long-standing claims and rights through digital tools and platforms, which have been essential for enacting discussions and creating new connections between diverse groups. Afro-Latinx Digital Connections includes both research articles and interviews with pra
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Brooks, Maegan Parker. Fannie Lou Hamer. Rowman & Littlefield, 2020. https://doi.org/10.5040/9798881821746.

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"[T]his is a testimonial to a courageous woman and her deep commitment to human rights." Booklist, Starred Review • An accessible biography of Fannie Lou Hamer that reveals pivotal moments within a remarkable life that spanned 59 tumultuous years in the history of American race relations. In 1964, Fannie Lou Hamer delivered a heart-wrenching testimony before the Democratic National Convention’s (DNC) Credentials Committee. In this speech, Hamer represented both the concerns of the Mississippi Freedom Democratic Party (MFDP) and the limits of American democracy when she proclaimed: “I question
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research
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Book chapters on the topic "Deep Echo State Networks"

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Dettori, Stefano, Ismael Matino, Valentina Colla, and Ramon Speets. "Deep Echo State Networks in Industrial Applications." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49186-4_5.

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Gallicchio, Claudio, and Alessio Micheli. "Reservoir Topology in Deep Echo State Networks." In Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_6.

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Tortorella, Domenico, Claudio Gallicchio, and Alessio Micheli. "Hierarchical Dynamics in Deep Echo State Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_55.

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Rodríguez-Ossorio, José Ramón, Claudio Gallicchio, Antonio Morán, Ignacio Díaz, Juan J. Fuertes, and Manuel Domínguez. "Deep Echo State Networks for Modelling of Industrial Systems." In Engineering Applications of Neural Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62495-7_9.

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Gallicchio, Claudio, and Alessio Micheli. "Richness of Deep Echo State Network Dynamics." In Advances in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20521-8_40.

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Scarpiniti, Michele, Sofia Perticarà, Yong-Cheol Lee, and Aurelio Uncini. "Deep Echo State Network for Environmental Sound Classification." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0994-9_7.

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Reddy, V. Dinesh, Kurinchi Nilavan, G. R. Gangadharan, and Ugo Fiore. "Forecasting Energy Consumption Using Deep Echo State Networks Optimized with Genetic Algorithm." In Artificial Intelligence, Machine Learning, and Data Science Technologies. CRC Press, 2021. http://dx.doi.org/10.1201/9781003153405-11.

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Xu, Baolei, and Qing Chen. "Deep Echo State Network Based Neuroadaptive Control for Uncertain Systems." In Neural Computing for Advanced Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6142-7_21.

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Abudalfa, Shadi, and Kevin Bouchard. "Hybrid Deep-Readout Echo State Network and Support Vector Machine with Feature Selection for Human Activity Recognition." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72802-1_11.

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Stojanovski, David, Uxio Hermida, Pablo Lamata, Arian Beqiri, and Alberto Gomez. "Echo from Noise: Synthetic Ultrasound Image Generation Using Diffusion Models for Real Image Segmentation." In Simplifying Medical Ultrasound. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44521-7_4.

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AbstractWe propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthe
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Conference papers on the topic "Deep Echo State Networks"

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Büyükbiçakci, Erdal, Cahit Atli, Istanbul Gedik, et al. "Machine Learning Enhancements for Electric Vehicles: Fault Detection through Deep Echo State Networks (ESN) Model." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10801032.

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Anchitaalagammai, J. V., S. Kavitha, S. Murali, S. Devi Sri Monica, and M. Harinidevi. "Optimized Echo State Network-Based Deep Learning Model for Intrusion Detection in Wireless Communication." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862947.

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Jiang, Xin, Lu Zhao, Wen Liu, et al. "Foundation Pit Support Deformation Prediction Based on Deep Echo State Network Optimized by Empirical Balance Particle Swarm Optimization." In 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC). IEEE, 2024. http://dx.doi.org/10.1109/aiotc63215.2024.10748306.

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Pompei, Geremia, Patrizio Dazzi, Valerio De Caro, and Claudio Gallicchio. "Decentralized Incremental Federated Learning with Echo State Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650756.

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Spinnato, Francesco, Andrea Cossu, Riccardo Guidotti, Andrea Ceni, Claudio Gallicchio, and Davide Bacciu. "Enhancing Echo State Networks with Gradient-based Explainability Methods." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-78.

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Gallicchio, Claudio, and Alessio Micheli. "Deep Tree Echo State Networks." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489464.

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Qiu, Wenqi, and Wu Ai. "A Decentralized Algorithm for Deep Echo State Networks." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9728101.

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Margin, Dan-Andrei, Iustin-Alexandru Ivanciu, and Virgil Dobrota. "Deep Reservoir Computing using Echo State Networks and Liquid State Machine." In 2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE, 2022. http://dx.doi.org/10.1109/blackseacom54372.2022.9858322.

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Pedrelli, Luca, Marco Tramontano, Giuseppe Vannozzi, and Andrea Mannini. "Deep Echo State Networks for Functional Ambulation Categories Estimation." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-149.

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Liu, Xuanlin, Mingzhe Chen, Changchuan Yin, and Walid Saad. "Analysis of Memory Capacity for Deep Echo State Networks." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00072.

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Reports on the topic "Deep Echo State Networks"

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Neighbor, Christopher. Minimum Complexity Echo State Networks for Genome and Sequence Analysis. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7376.

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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Rémy, Elisabeth, Romain Escudier, and Alexandre Mignot. Access impact of observations. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.8.

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The accuracy of the Copernicus Marine Environment and Monitoring Service (CMEMS) ocean analysis and forecasts highly depend on the availability and quality of observations to be assimilated. In situ observations are complementary to satellite observations that are restricted to the ocean surface. Higher resolution model forecasts are required by users of the CMEMS global and regional ocean analysis and forecasts. To support this with an efficient observational constrain of the model forecast via data assimilation, an increase observation coverage is needed, associated with an improved usage of
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