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 14 of 14 for “"echo-state networks"”.
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MIMO-OFDM Symbol Detection via Echo State Networks
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 …
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Deep Reinforcement Learning for Next Generation Wireless Networks with Echo State Networks
… applications in the 5G and future 6G wireless networks, the available training data is very limited. Therefore, it is important to develop DRL strategies that are capable of capturing the temporal correlation of the dynamic environment that only requires limited training overhead. In this …
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NEUROEVOLUTION AND AN APPLICATION OF AN AGENT BASED MODEL FOR FINANCIAL MARKET
… their actions. Agent decisions are based on Echo State Networks. The agents take various market indicators as inputs and produce an action such as: buy or sell. We optimize the parameters of the echo state networks using evolutionary algorithms.</p>
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Optimisation of chaotic thermoacoustics
… is based on reservoir computing, in particular, echo state networks. We analyse the predictive capabilities of echo state networks both in the short- and long-time predictions of the dynamics. We find that both fully data-driven and model-informed architectures are able to predict the chaotic …
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Neural networks for the prediction of chaos and turbulence
… spatiotemporal chaos. The methods are based on echo state networks (ESNs), which are versatile recurrent neural networks that learn temporal correlations within data. In the first part of the thesis, we focus on the network's hyperparameters, which markedly affect the performance of the machine. …
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Neural Decomposition of Time-Series Data for Effective Generalization
… basis function, Gashler and Ashmore’s model, and echo state networks.</p>
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Approximation of Large Stiff Acausal Models
… generate surrogates, called the Continuous-Time Echo State Networks (CTESN), that can capture multiple widely separated time-scales which is easy to automate. We comment on its implementa- tion and then propose an active learning scheme for adaptively choosing training points. We then present …
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Deep neural network acoustic models for multi-dialect Arabic speech recognition
… time varying signals. Artificial Neural Networks (ANNs) have also been widely used for representing time varying quasi-stationary signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language …
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Biologically inspired goal directed navigation for mobile robots
… and a bio-inspired approach using an Echo State Network and Liquid State Machine architecture was chosen as the base for the navigation modules. The navigation module implemented in this work is trained to navigate and localise itself in different environments drawing its inspiration …
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Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data
… mathematically dynamic changes in biological networks and DNA copy numbers, to develop machine learning algorithms to learn these statistical models from high-throughput biological data, and to demonstrate their applications in systems biological studies. The first part (Chapters 2-4) of the …
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On the induction of temporal structure by recurrent neural networks
… to model human language. Simple Recurrent Networks (SRNs) are a class of so-called artificial neural networks that have a long history in language modelling via learning to predict the next word in a sentence. However, SRNs have also been shown to suffer from catastrophic forgetting, lack …
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Spectrum Management in Dynamic Spectrum Access: A Deep Reinforcement Learning Approach
… 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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Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach
Recurrent neural networks (RNNs) have been widely used for supervised pattern recognition and exploring the underlying spatio-temporal correlation. However, due to the vanishing/exploding gradient problem, training a fully connected RNN in many cases is very difficult or even impossible. The …
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Applying Reservoir Computing for Driver Behavior Analysis and Traffic Flow Prediction in Intelligent Transportation Systems
… to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector …