| dc.description.abstract | Over the last few years, neural networks have become extremely popular, and their usage
is increasing rapidly. This project has investigated the use of neural networks for one-step
time series forecasting on highly random data. Multi-layer perceptron (MLP), convolutional
neural networks (CNN), recurrent neural networks (RNN), and long short-term
memory (LSTM) cells are tested to see if they can give a binary classification accuracy
above 50% using this data. The assignment focuses on designing a small embedded neural
network with low latency.
The different neural network architectures are built using a deep learning library in
Python, called Keras. This is a high-level software framework, built on top of either Tensorflow or Theano, for fast and easy prototyping of neural networks.
The conclusion of the study is that only the CNN satisfied the requirements of the
assignment during the work of this thesis. None of the other architectures showed sign
of learning generalized patterns and structures from the dataset in question. The CNN
showed the most promising results, being able to extract information about the training
set that increased the classification accuracy of the test. This leads the way for further
development and an eventual hardware implementation of the inference phase reducing
the run-time latency. | |
