Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 27 for “"Reservoir computing"”.
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Reservoir Computing: computation with dynamical systems
… Deze methodes worden aangeduid met de naam Reservoir Computing. Reservoir Computing combineert de indrukwekkende rekenkracht van recurrente neurale netwerken met een eenvoudige trainingsmethode. Bovendien blijkt dat deze trainingsmethoden niet beperkt zijn tot neurale netwerken, maar kunnen …
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FPGA Reservoir Computing Networks for Dynamic Spectrum Sensing
… hardware implementations of the delayed feedback reservoir (DFR) model show promising results for meeting these constraints while achieving high accuracy in cognitive radio applications. This thesis answers two research questions surrounding the applicability of FPGA DFR systems for DSS. First, …
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Optimizing Reservoir Computing Architecture for Dynamic Spectrum Sensing Applications
Spectrum sensing in wireless communications serves as a crucial binary classification tool in cognitive radios, facilitating the detection of available radio spectrums for secondary users, especially in scenarios with high Signal-to-Noise Ratio (SNR). Leveraging Liquid State Machines (LSMs), which …
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Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach
… of training traditional RNNs, led us to reservoir computing (RC) which recently attracted a lot of attention due to its simple training methods and fixed weights at its recurrent layer. There are three different categories of RC systems, namely, echo state networks (ESNs), liquid state …
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Reinforcement learning using reservoir computing for soft robotic control: A bio-inspired system
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01
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Brain-machine interface coupled cognitive sensory fusion with a Kohonen and reservoir computing scheme
… of embodied cognition is used to interact with a reservoir computing recurrent neural network in an attempt to produce simple language interaction, e.g. babbling, from the child android.
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Applying Reservoir Computing for Driver Behavior Analysis and Traffic Flow Prediction in Intelligent Transportation Systems
… detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle …
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Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation
… and massive MIMO-OFDM systems by utilizing reservoir computing, extreme learning machine, multi-mode reservoir computing, and StructNet; 2) Channel estimation, where residual learning-based offline method is introduced for WiFi-OFDM systems, and a StructNet-based online method is devised for …
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Theory and applications of multifunctional reservoir computers
… biological to artificial neural networks via the reservoir computing machine learning paradigm. Multifunctionality describes the ability of a single neural network that exploits a form of multistability to perform a multitude of mutually exclusive tasks. The dynamics of multifunctional RCs are …
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Two-Phase Buck Converter Optimize by Echo State Network
… can treat as the connection between two nodes. Reservoir computing can be seen as an extension of the neural network since it is a framework for computation. Echo State Network(ESN) is one of the major types of reservoir computing, and it is a recurrent neural network. Compared with a neural …
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Enhancing Quadruped Robot Design With Intelligent Physics-Informed Neural Network-Assisted Dynamic State Estimation and Active Spine Integration
… the physical model estimation is enhanced, and Reservoir Computing is employed for real-time adaptive control. This significantly improves robotic mobility and stability in challenging environments.
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Machine Learning-Based Receiver in Multiple Input Multiple Output Communications Systems
… the neural network structures are rooted in reservoir computing - an efficient neural network computational framework with decent generalization performance for limited training datasets. Therefore, the resulting neural network structures can learn beyond observation and offer decent …
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Enabling Energy-Efficient Hybrid CMOS and Embedded Memory Accelerators for Neuromorphic Computing at the Edge
… constraints. The first contribution explores computing-in-memory (CIM) architectures using memristors, which combine storage and computation to reduce data movement. While memristors offer density, low power, and nonvolatility, challenges such as resistance variation degrade inference …
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Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations
… to completely different patterns of activity. Reservoir computing is one of the first frameworks that provided an efficient solution for biologically relevant neural networks to learn complex temporal tasks in the presence of chaos. We showed that although reservoirs (i.e. recurrent neural …
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Towards Explainability and Domain Knowledge-inspired Design of Online Real-Time Learning Techniques in NextG Wireless Systems
… online real-time learning architectures based on reservoir computing (RC). The effectiveness of RC in orthogonal frequency division multiplexing (OFDM) and MIMO-OFDM receive processing is established from the ground up with first principles, resulting in enhanced explainability and …
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Towards Real-World Quantum Machine Learning
… (WO2025073041A1). Third, I propose a quantum reservoir computing (QRC) scheme that reuses a fixed quantum feature-map circuit as the reservoir while injecting temporal memory via an explicit feedback loop. The register is recycled across time, so quantum resources remain constant, no quantum …
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Efficient and Generalizable Machine Learning Models for Predicting Complex Dynamics
… data. In this dissertation, we first show that reservoir computing—a simple, efficient, and versatile framework for data-driven modeling of dynamical systems—can generalize to unexplored regions of state space without explicit structural priors. Using multistable dynamical systems as a test …
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Analyzing the Dynamics of Biological and Artificial Neural Networks with Applications to Machine Learning
… networks in the brain, within the framework of reservoir computing. We emphasize the stabilizing role of inhibition in reservoir computers (RCs), mirroring its function in the brain. We propose a novel inhibitory adaptation mechanism that allows RCs to autonomously adjust inhibitory connections …
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Connectome-Constrained Artificial Neural Networks
… these onto a multilayer perceptron (MLP) and a reservoir computer (RC), in order to craft “fruit fly neural networks” (FFNNs). We study the impact on performance, variance, and prediction dynamics from using FFNNs compared to non-FFNN models on odour classification, chaotic time-series …
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