Πρόβλεψη τροχιάς αεροσκάφους με την χρήση νευρωνικών δικτύων LSTM
Prediction of aircraft trajectory using LSTM neural networks
Long Short-Term Memory (LSTM) is a neural network (RNN) feedback architecture designed to approach and model time sequences and their broader dependencies more accurately than other RNN types. In this study, we will use LSTM RNNs to classify flight paths. As we said, this type of RNN is very effective for predicting and classifying length sequences. We will also take advantage of the fact that RNNs can accept sequences of variable length as inputs. We will process and modify the data in order to be able to use it in our analysis. The data will be latitude, latitude and altitude, speed, humidity, etc. Finally, we will build the LSTM RNN and optimize its parameters to achieve fairly high accuracy and then present the results.