Sökning: "historical consistent neural networks"

Hittade 3 uppsatser innehållade orden historical consistent neural networks.

1. 1. Exploring improvements of wind power forecasts using Convolutional Neural Networks and Time Series Analysis

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Jakob Nabialek; [2022]
   Nyckelord :wind power forecasting; convolutional neural networks; kalman filter; electricity market; day-ahead market; Mathematics and Statistics;

   Sammanfattning : Due to environmental considerations, volumes of renewable power production are rapidly growing, and its share of the energy pool is increasing. The inter- mittent nature of wind power, being one of the main renewable energy sources, is a challenge when generating production forecasts. LÄS MER

3. 2. Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks

   Uppsats för yrkesexamina på avancerad nivå, Blekinge Tekniska Högskola/Institutionen för programvaruteknik

   Författare :Jakob Ahrneteg; Dean Kulenovic; [2019]
   Nyckelord :semantic segmentation; page segmentation; recurrent neural network; layout analysis; semantisk segmentering; dokument segmentering; recurrent neural network; layout analys;

   Sammanfattning : Background. This thesis focuses on the task of historical document semantic segmentation with recurrent neural networks. Document semantic segmentation involves the segmentation of a page into different meaningful regions and is an important prerequisite step of automated document analysis and digitisation with optical character recognition. LÄS MER

4. 3. Implementation and Evaluation of Historical Consistent Neural Networks Using Parallel Computing

   Master-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska högskolan

   Författare :Johan Bjarnle; Elias Holmström; [2015]
   Nyckelord :neural networks; finance; mathematics; hcnn; cuda; historical consistent neural networks; johan bjarnle; elias holmström;

   Sammanfattning : Forecasting the stock market is well-known to be a very complex and difficult task, and even by many considered to be impossible. The new model, emph{Historical Consistent Neural Networks} (HCNN), has recently been successfully applied for prediction and risk estimation on the energy markets. HCNN is developed by Dr. LÄS MER