Essays about: "Recurrent Neural Networks RNNs"
Showing result 1 - 5 of 30 essays containing the words Recurrent Neural Networks RNNs.
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1. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks
University essay from Uppsala universitet/Statistiska institutionenAbstract : Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). READ MORE
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2. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition
University essay from KTH/Mekatronik och inbyggda styrsystemAbstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE
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3. Assessing Electricity Prices and Their Driving Mechanisms in Brazil with Neural Networks
University essay from KTH/Skolan för industriell teknik och management (ITM)Abstract : In general, electricity prices are very volatile and derive from many external variables. In Brazil, this price is determined by computer models developed and operated by government organizations. The supply and demand relationships are not enough to determine prices in Brazilian submarkets. READ MORE
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4. Data Driven Model Identification for Remote Electrical Tilt Systems
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : 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. READ MORE
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5. Temporal Localization of Representations in Recurrent Neural Networks
University essay from Högskolan Dalarna/Institutionen för information och teknikAbstract : 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. READ MORE
