LSTMbaserade
LSTMbaserade refers to models or approaches based on Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN) specifically designed to process and predict sequential data. Introduced by Hochreiter and Schmidhuber in 1997, LSTM networks are distinguished by their ability to maintain and access information over extended sequence lengths, addressing the vanishing gradient problem common in traditional RNNs.
LSTM architectures consist of memory cells equipped with gating mechanisms—input, forget, and output gates—that regulate the
In practical applications, LSTM-based systems are typically trained using large datasets and optimized through gradient descent
Research and development continue to evolve around LSTM-based approaches, often integrating them with other neural network