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Development of a Model for Predicting Brake Torque Using LSTM and TCN Models
Tomaž Roškar, 2020, master's thesis

Abstract: The main purpose of this thesis is to compare two state-of-the-art machine learning models, LSTM (Long Short-Term Memory) and TCN (Temporal Convolutional Network), on an AVL List GmbH case use, where the goal is to predict vehicle brake torque. Dataset used for model testing consists of multiple features which are preprocessed using several preprocessing methods. For model implementation Python’s libraries Keras and TensorFlow are used. Results from this thesis show that TCN is able to outperform LSTM. TCN achieves lower RMSE on the test dataset and is significantly faster in training and evaluation.
Keywords: brake torque, machine learning, neural network, LSTM, TCN, RNN, CNN
Published: 24.09.2020; Views: 207; Downloads: 0

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