Sökning: "Feed Forward Neural Networks"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden Feed Forward Neural Networks.

  1. 1. Recurrent neural networks in electricity load forecasting

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

    Författare :Samiul Alam; [2018]
    Nyckelord :Recurrent neural networks electricity load forecasting lstm renewable energy;

    Sammanfattning : In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting. The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. LÄS MER

  2. 2. Recurrent neural networks for financial asset forecasting

    Master-uppsats, KTH/Matematisk statistik

    Författare :Gustaf Tegnér; [2018]
    Nyckelord :;

    Sammanfattning : The application of neural networks in finance has found renewed interest in the past few years. Neural networks have a proven capability of modeling non-linear relationships and have been proven widely successful in domains such as image and speech recognition. LÄS MER

  3. 3. Model comparison of patient volume prediction in digital health care

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

    Författare :Sasha Hellstenius; [2018]
    Nyckelord :Recurrent Neural Networks; LSTM; Patient Volume Prediction; Digital Healthcare;

    Sammanfattning : Accurate predictions of patient volume are an essential tool to improve resource allocation and doctor utilization in the traditional, as well as the digital health care domain. Varying methods for patient volume prediction within the traditional health care domain has been studied in contemporary research, while the concept remains underexplored within the digital health care domain. LÄS MER

  4. 4. Forecasting Cloud Resource Utilization Using Time Series Methods

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

    Författare :Prashant Kumar; [2018]
    Nyckelord :;

    Sammanfattning : With the contemporary technological advancements, the adoption of cloud as service has been evolving exponentially while providing a seemingly incessant measure of resources such as storage, network, CPU and many more. In today’s data centres that accommodate thousands of servers, ensuring the availability of continuous services is a significant hurdle. LÄS MER

  5. 5. A Neural Networks Approach to Portfolio Choice

    Master-uppsats, KTH/Matematisk statistik

    Författare :Younes Djehiche; [2018]
    Nyckelord :;

    Sammanfattning : This study investigates a neural networks approach to portfolio choice. Linear regression models are extensively used for prediction. With the return as the output variable, one can come to understand its relation to the explanatory variables the linear regression is built upon. LÄS MER