Recurrent Neural Network ( RNN )
Kamal Al‐Malah · 2023
A Recurrent Neural Network (RNN) is a type of artificial neural network designed to process sequential data by maintaining an internal memory or state. RNNs have been successfully applied to various tasks involving sequential data, such as natural language processing, speech recognition, machine translation, sentiment analysis, and time series forecasting. However, traditional RNNs can struggle to capture long-term dependencies due to the “vanishing gradient” problem, where the gradients that flow backward in time can diminish exponentially. To address this, variants of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit, have been developed, which incorporate additional mechanisms to alleviate the vanishing gradient problem and enhance the memory capabilities of the network. The chapter shows how to predict the Remaining Useful Life of engines by using deep learning. It also shows how to classify text data using a deep learning LSTM network. The chapter explains how to train a deep learning LSTM network.