Sökning: "Gated Recurrent Units"

Visar resultat 1 - 5 av 16 uppsatser innehållade orden Gated Recurrent Units.

  1. 1. Data Driven Model Identification for Remote Electrical Tilt Systems

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Raphaël Ashruf; [2023]
    Nyckelord :;

    Sammanfattning : This thesis explores the use of supervised machine learning for modelling the dynamics of Remote Electrical Tilt (RET) telecom systems. Three methodologies, including linear regressionfor linear dynamics models, Gaussian Process (GP) regression, and Recurrent Neural Networks (RNN) with Gated Recurrent Units (GRU) are proposed. LÄS MER

  3. 2. Temporal Localization of Representations in Recurrent Neural Networks

    Master-uppsats, Högskolan Dalarna/Institutionen för information och teknik

    Författare :Asadullah Najam; [2023]
    Nyckelord :Recurrent Neural Networks RNNs ; Deep Learning; Time Series Prediction; Exploding Values; Gradient Decay; Long Short-Term Memory LSTMs ; Gated Recurrent Units GRUs ; Attention Mechanism; Moving Representations; Localizing Representations;

    Sammanfattning : Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechanisms remain unclear. LÄS MER

  4. 3. Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Sara Moazez Gharebagh; [2023]
    Nyckelord :TensorFlow.js; JavaScript; Artificial Neural Networks; Deep Learning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units; TensorFlow.js; JavaScript; Artificiella Neurala Nätverk; Djupinlärning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units;

    Sammanfattning : Deep Learning is a powerful and rapidly advancing technology that has shown promising results within the field of weather forecasting. Implementing and using deep learning models can however be challenging due to their complexity. LÄS MER

  5. 4. Predicting Waveforms with Machine Learning for Efficient Triggering in Monitoring Systems

    Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

    Författare :Amanda Rautio; [2023]
    Nyckelord :;

    Sammanfattning : Energy systems need to evolve to meet the requirements of the modern world and the future. Hence, substantial effort is needed at an academic and industrial level to develop valuable diagnostic techniques. LÄS MER

  7. 5. Graph Neural Network for Traffic Flow Forecasting : Does an enriched adjacency matrix with low dimensional dataenhance the performance of GNN for traffic flow forecasting?

    Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Fredrik Kortetjärvi; Rohullah Khorami; [2023]
    Nyckelord :Graph Neural Network;

    Sammanfattning : Nowadays, machine learning methods are used in many applications and deployed in manyelectronic devices to solve problems and predict future states. One of the challenges mostbig cities confront is traffic jams since the roads are crammed with more and more vehicles, which will easily cause traffic congestion. LÄS MER