Essays about: "återkommande neuralt nätverk"

Showing result 11 - 15 of 15 essays containing the words återkommande neuralt nätverk.

1. 11. A Recurrent Neural Network For Battery Capacity Estimations In Electrical Vehicles

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

   Author : Simon Corell; [2019]
   Keywords : Recurrent Neuralt Network; LSTM; Linear Regression; Lithium-Ion battery; Data pre-processing; Feature Selection.;

   Abstract : This study is an investigation if a recurrent long short-term memory (LSTM) based neural network can be used to estimate the battery capacity in electrical cars. There is an enormous interest in finding the underlying reasons why and how Lithium-ion batteries ages and this study is a part of this broader question. READ MORE

3. 12. Time series Forecast of Call volume in Call Centre using Statistical and Machine Learning Methods

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Nicoló Baldon; [2019]
   Keywords : Time Series Analysis; SARIMA; Seasonal ANN; LSTM; Call Center data; seasonal time series;

   Abstract : Time series is a collection of points gathered at regular intervals. Time series analysis explores the time correlations and tries to model it according to trend and seasonality. One of the most relevant tasks, in time series analysis, is forecasting future values, which is considered fundamental in many real-world scenarios. READ MORE

4. 13. An evaluation of deep neural network approaches for traffic speed prediction

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Cosar Ghandeharioon; [2018]
   Keywords : Deep Learning; Regression; Time Series; LSTM; Neural decomposition.; Djupinlärning; Regression; Tidsserier; LSTM; Neural dekomposition.;

   Abstract : The transportation industry has a significant effect on the sustainability and development of a society. Learning traffic patterns, and predicting the traffic parameters such as flow or speed for a specific spatiotemporal point is beneficial for transportation systems. READ MORE

5. 14. Scalable System-Wide Traffic Flow Predictions Using Graph Partitioning and Recurrent Neural Networks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Jón Reginbald Ivarsson; [2018]
   Keywords : Traffic Flow Prediction; Machine Learning; Recurrent Neural Network; Graph Partitioning; Big Data; Trafikprognoser; Maskininlärning; Återkommande Neuralt Nätverk; Graf Partitionering; Big Data;

   Abstract : Traffic flow predictions are an important part of an Intelligent Transportation System as the ability to forecast accurately the traffic conditions in a transportation system allows for proactive rather than reactive traffic control. Providing accurate real-time traffic predictions is a challenging problem because of the nonlinear and stochastic features of traffic flow. READ MORE

6. 15. Predicting Customer Churn Using Recurrent Neural Networks

   University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

   Author : Jesper Ljungehed; [2017]
   Keywords : churn prediction; churn; prediction; recurrent neural network; rnn; customer lifetime value; clv;

   Abstract : Churn prediction is used to identify customers that are becoming less loyal and is an important tool for companies that want to stay competitive in a rapidly growing market. In retail, a dynamic definition of churn is needed to identify churners correctly. Customer Lifetime Value (CLV) is the monetary value of a customer relationship. READ MORE