Essays about: "historical consistent neural networks"

Found 3 essays containing the words historical consistent neural networks.

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

    University essay from Lunds universitet/Matematisk statistik

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

    Abstract : 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. READ MORE

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 Semantic Segmentation of Historical Document Images Using Recurrent Neural Networks

    University essay from Blekinge Tekniska Högskola/Institutionen för programvaruteknik

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

    Abstract : 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 This thesis focuses on the task of historical document semantic segmentation with recurrent neural networks.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. READ MORE

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

    University essay from Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska högskolan

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

    Abstract : 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. READ MORE