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

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Published: 5 June 2025

Last updated: 1 August 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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10

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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11

RUEDA, CAMILO VELASCO. "ESNPREDICTOR: TIME SERIES FORECASTING APPLICATION BASED ON ECHO STATE NETWORKS OPTIMIZED BY GENETICS ALGORITHMS AND PARTICLE SWARM OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24785@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>A previsão de séries temporais é fundamental na tomada de decisões de curto, médio e longo prazo, em diversas áreas como o setor elétrico, a bolsa de valores, a meteorologia, entre outros. Tem-se na atualidade uma diversidade de técnicas e modelos para realizar essas previsões, mas as ferramentas estatísticas são as mais utilizadas principalmente por apresentarem um maior grau d

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12

Pettersson, Fredrik. "Optimizing Deep Neural Networks for Classification of Short Texts." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76811.

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This master's thesis investigates how a state-of-the-art (SOTA) deep neural network (NN) model can be created for a specific natural language processing (NLP) dataset, the effects of using different dimensionality reduction techniques on common pre-trained word embeddings and how well this model generalize on a secondary dataset. The research is motivated by two factors. One is that the construction of a machine learning (ML) text classification (TC) model is typically done around a specific dataset and often requires a lot of manual intervention. It's therefore hard to know exactly what proce

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13

CAMPEDELLI, GIAN MARIA. "ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM." Doctoral thesis, Università Cattolica del Sacro Cuore, 2020. http://hdl.handle.net/10280/70552.

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Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terr

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14

Zhang, Bai. "Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/29111.

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Biological systems constantly evolve and adapt in response to changed environment and external stimuli at the molecular and genomic levels. Building statistical models that characterize such dynamic changes in biological systems is one of the key objectives in bioinformatics and computational biology. Recent advances in high-throughput genomic and molecular profiling technologies such as gene expression and and copy number microarrays provide ample opportunities to study cellular activities at the individual gene and network levels. The aim of this dissertation is to formulate mathematically d

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15

Bettaieb, Luc Alexandre. "A Deep Learning Approach To Coarse Robot Localization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1493646936728041.

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16

Mazumdar, Joy. "System and method for determining harmonic contributions from nonlinear loads in power systems." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/23215.

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The objective of this research is to introduce a neural network based solution for the problem of measuring the actual amount of harmonic current injected into a power network by an individual nonlinear load. Harmonic currents from nonlinear loads propagate through the system and cause harmonic pollution. As a result, voltage at the point of common coupling (PCC) is rarely sinusoidal. The IEEE 519 harmonic standard provides customer and utility harmonic limits and many utilities are now requiring their customers to comply with IEEE 519. Measurements of the customer’s current at the PCC are exp

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17

Charles, Adam Shabti. "Dynamics and correlations in sparse signal acquisition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53592.

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One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and in time-scales relevant to the tasks critical to system performance. This classical concept of efficient signal acquisition has been a cornerstone of signal processing research, spawning traditional sampling theorems (e.g. Shannon-Nyquist sampling), efficient filter designs (e.g. the Parks-McClellan algorithm), novel VLSI chipsets for embedded systems, and optimal tracking algorithms (e.g. Kalman filtering). Traditional techniques h

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18

Mrkšić, Nikola. "Data-driven language understanding for spoken dialogue systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/276689.

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Spoken dialogue systems provide a natural conversational interface to computer applications. In recent years, the substantial improvements in the performance of speech recognition engines have helped shift the research focus to the next component of the dialogue system pipeline: the one in charge of language understanding. The role of this module is to translate user inputs into accurate representations of the user goal in the form that can be used by the system to interact with the underlying application. The challenges include the modelling of linguistic variation, speech recognition errors

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19

Alomar, Barceló Miquel Lleó. "Methodologies for hardware implementation of reservoir computing systems." Doctoral thesis, Universitat de les Illes Balears, 2017. http://hdl.handle.net/10803/565422.

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[cat]Inspirades en la forma en què el cervell processa la informació, les xarxes neuronals artificials (XNA) es crearen amb l’objectiu de reproduir habilitats humanes en tasques que són difícils de resoldre mitjançant la programació algorítmica clàssica. El paradigma de les XNA s’ha aplicat a nombrosos camps de la ciència i enginyeria gràcies a la seva capacitat d’aprendre dels exemples, l’adaptació, el paral·lelisme i la tolerància a fallades. El reservoir computing (RC), basat en l’ús d’una xarxa neuronal recurrent (XNR) aleatòria com a nucli de processament, és un model de gran abast molt a

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20

Carbajal, Guillaume. "Apprentissage profond bout-en-bout pour le rehaussement de la parole." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0017.

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Cette thèse s'insère dans le développement des systèmes de télécommunication mains-libres, en particulier avec des enceintes intelligentes en environnement domestique. L'utilisateur interagit avec un correspondant distant en étant généralement situé à quelques mètres de ce type de système. Les microphones sont susceptibles de capter des sons de l'environnement qui se mêlent à la voix de l'utilisateur, comme le bruit ambiant, l'écho acoustique et la réverbération. Ces types de distorsions peuvent gêner fortement l'écoute et la compréhension de la conversation par le correspondant distant, et il

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21

Hung, Wei-Jung, and 洪瑋嶸. "Television Echo Cancellation with Microphone Array and Frequency-Domain Deep Recurrent Neural Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/6turwe.

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碩士<br>國立臺北科技大學<br>電子工程系碩士班（碩士在職專班）<br>105<br>The purpose of this research was to study TV program echo cancellation under the situation of smart TV. We expect the receiver can receive the clean speech command without being disturbed by continuous playing TV program. The survey was conducted by several subjects. First, our echo cancellation system was deal with MVDR that can reach the ability of strengthened speech signal from the source pointed to USER and echo noise inhibition. Then, we use frequency-domain RNN as Adaptive Filter to cancel TV echo. Final, the remaining echo will be filtered o

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22

FAN, CHUNG-HAO, and 范仲豪. "S&P 500 Forecasting by Observer/Kalman filter IDentification and Echo State Networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27892442500509554323.

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碩士<br>國立高雄海洋科技大學<br>電訊工程研究所<br>102<br>In general, the changes of the stock market index are mostly non-linear, so it is unsuitable to use a linear system to forecast. The observer / Kalman filter identification (OKID) is based on the linear system which can simulate nonlinear systems to achieve the purpose of forecast. This article attempts to use the Observer / Kalman filter Identification method (OKID) and Echo State Networks (ESN) as the tools of Standard & Poor's 500 (S & P500) index forecast system, forecast the day HIGH, LOW and the CLOSE price, and compare the advantages and disadvanta

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23

CHEN, YU-ZHONG, and 陳昱中. "An On-line System for Estimating Die State Based on Deep Neural Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/xwkpfv.

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碩士<br>國立高雄第一科技大學<br>電機工程研究所碩士班<br>106<br>Die wear, e.g. changes of die dimensions and forming space, can be caused from accumulated impacts and frictions in the forming stage of producing metal fastener. It could reduce forming accuracy and increase production costs due to high defect rates. Because observing the die internal structure is inefficient, experienced experts are needed to adjust or replace the die by observing the fastener external quality. Besides, the poor production environment induces high employee turnover. The challenge of the fastener forming industry is how to automaticall

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24

Matzner, Filip. "Maximalizace výpočetní síly neuroevolucí." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-352614.

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Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Afterwards, the best performing echo state network is compared to a network evolved via neuroevolution. The evolved network outperforms the best echo state network, however, the evolution consumes significant computational resources. By combining the best of both worlds, the simplicity of

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25

Parajuli, Janak. "Extracting surface water bodies from sentinel-2 imagery using convolutional neural networks." Master's thesis, 2021. http://hdl.handle.net/10362/113707.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies<br>Water is an integral part of eco-system with significant role in human life. It is immensely mobilized natural resource and hence it should be monitored continuously. Water features extracted from satellite images can be utilized for urban planning, disaster management, geospatial dataset update and similar other applications. In this research, surface water features from Sentinel-2 (S2) images were extracted using state-of-the-art approaches of deep learning.

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(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices." 2019.

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<p>Deep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (A

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Daouda, Tariq. "Génération et reconnaissance de rythmes au moyen de réseaux de neurones à réservoir." Thèse, 2010. http://hdl.handle.net/1866/4931.

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Les réseaux de neurones à réservoir, dont le principe est de combiner un vaste réseau de neurones fixes avec un apprenant ne possédant aucune forme de mémoire, ont récemment connu un gain en popularité dans les communautés d’apprentissage machine, de traitement du signal et des neurosciences computationelles. Ces réseaux qui peuvent être classés en deux catégories : 1. les réseaux à états échoïques (ESN)[29] dont les activations des neurones sont des réels 2. les machines à états liquides (LSM)[43] dont les neurones possèdent des potentiels d’actions, ont été appliqués à différentes tâches [11

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