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 10 of 10 for “"feedforward networks"”.
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Universal and succinct source coding of deep neural networks
Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device …
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Recurrent convolutional neural networks as models of biological object recognition
Deep feedforward neural network models of vision dominate in both computational neuroscience and engineering. However, the primate visual system contains abundant recurrent connections. In this thesis, we investigate the addition of recurrent connections to the popular framework of convolutional …
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An investigation into the use of neural networks for the prediction of the stock exchange of Thailand
… predictions are complicated tasks. Neural networks are one of the popular approaches used for research on stock market forecast. This study developed neural networks to predict the movement direction of the next trading day of the Stock Exchange of Thailand (SET) index. The SET has yet to …
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Development and Evaluation of Generative Adversarial Networks for Predicting Central Hemodynamics
… investigates the use of generative adversarial networks (GANs) in combination with cardiovascular mechanistic models to estimate central hemodynamic values from ECG and tabular data. Three hemodynamic quantities form the focus of this work: mean pulmonary artery pressure (mPAP), mean pulmonary …
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A sensory system for robots using evolutionary artificial neural networks.
… Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network …
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Quantitative convergence analysis of dynamical processes in machine learning
… of different architectures, deep residual networks (ResNets), and deep feedforward networks (FFNets). By taking these architectures as iterative maps and analyzing their convergence via neural tangent kernel, we prove that deep ResNets can effectively separate data while deep FFNets …
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Intelligent autopilots for ships
… The learning and adaptive features of neural networks and fuzzy logic systems are exploited and used to solve advantageously the control design problem. Adaptive networks are used as a unifying structure where different kinds of neural networks and fuzzy logic paradigms can be described. In …
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Symbolic and connectionist machine learning techniques for short-term electric load forecasting
… techniques to present symbolic data for neural networks. Also, multilayer feedforward networks trained by the backpropagation algorithm perform poorly in forecasting chaotic patterns such as those encountered in peak load demand. Symbolic machine learning techniques are powerful concept …
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Transition equity markets of Central Europe: volatility, predictability, integration
… forecast performance of linear and nonlinear (feedforward networks) conditional mean estimators with past trading signals in the conditional mean equation indicates substantial forecast improvements of the feedforward network regression. Chapter Five addresses the issue of integration of the …
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Neural networks in control engineering
… investigate the viability of integrating neural networks into control structures. These networks are an attempt to create artificial intelligent systems with the ability to learn and remember. They mathematically model the biological structure of the brain and consist of a large number of simple …