Long Short-Term Memory (LSTM) - function and application

Artificial intelligence (AI) has made enormous progress in recent years and is revolutionizing the world as we know it. One of the most significant developments is the so-called Long Short-Term Memory (LSTM). In this article, we will take an in-depth look at LSTM, how it works and what advantages it offers.

What is a Long Short-Term Memory?

An LSTM is a special functional block of recurrent neural networks (RNNs) with a long-term short-term memory. It is an evolution of RNNs and helps to solve the vanishing gradient problem, in which during training the gradients of the weights become smaller and smaller and therefore the network no longer stores useful information. LSTM cells have three types of gates-an input gate, a memory and forgetting gate, and an output gate-to store memories of past experiences. Short-term memory is retained for a long time and the behavior of the network is encoded in the weights. LSTM networks are particularly useful for making predictions based on time-series data, such as handwritten text recognition and speech recognition.

How does a Long Short-Term Memory work?

An LSTM network consists of multiple LSTM cells arranged in a sequential array. Each LSTM cell has three gates that control the flow of information in the network. The input gate regulates the flow of information, the remember and forget gate ensures that unimportant information is forgotten, and the output gate determines what information is passed on to the next step. In this way, the network can make decisions based on past experience and is able to recognize long-term dependencies in the data.

Applications of Long Short-Term Memory

  1. Speech recognitionLSTM is often used in speech recognition tools. The technology can recognize and analyze the speaker's linguistic patterns to identify him or her. It can also enable automatic speech recognition, for example to control voice commands for smart home devices.
  2. Handwriting recognitionLSTM can also help to recognize handwritten texts. The system can analyze and distinguish the writing patterns in order to correctly identify the text. This allows it to be used in word processing programs for handwritten input, for example.
  3. Prediction of time series dataLSTM can be used to predict future events based on time series data. This can be used, for example, in the prediction of financial markets, weather forecasts or the prediction of electricity demand and energy supply.
  4. Anomaly detection in network trafficLSTM can also help to detect anomalies in network traffic. By analyzing patterns and comparing them with historical data, the system can detect unusual activity and identify possible attacks.
  5. Intrusion Detection Systems: IDS use LSTM to detect possible attacks on systems or networks. The system can analyze the activities of hackers and take appropriate measures to ensure the security of the networks.

LSTM Functionality

LSTM is a type of recurrent neural network (RNN) designed to solve the vanishing gradient problem by introducing a memory cell that can store information for longer periods of time. The LSTM architecture consists of several important components.


Input gate

Controls the flow of new inputs into the memory cell. It uses a sigmoid activation function to decide which values to keep and which to discard.

Forget Gate

Controls the flow of information from the previous time step that should be forgotten. It also uses a sigmoid activation function to determine which information should be forgotten.

Output gate

Controls the output from the memory cell. It uses a sigmoid activation function and a hyperbolic tangent function to determine what information to output.

Memory Cell

The main component of the LSTM architecture. It stores information over time and can selectively forget information or add new information to its internal state.

At each time step, the LSTM model receives an input vector and a hidden state vector from the previous time step. The input vector is processed by the Input Gate and the Forget Gate and the resulting values are used to update the Memory Cell.

A candidate state is then generated using the Input Gate and this candidate state is combined with the Memory Cell state using an element-wise addition operation. Finally, the output gate is used to determine what information to output from the memory cell, and the resulting hidden state vector is passed to the next time step.

By using a memory cell and three separate gates to control the flow of information, the LSTM architecture is able to effectively learn and store information over extended periods of time, making it suitable for tasks such as speech recognition, language translation, and sentiment analysis.

Comparison of LSTM with other techniques

LSTM networks have found many applications in AI, from speech recognition to anomaly detection in network traffic. The ability to select and forget past information makes it possible for networks to learn what past information is useful for current output based on training data. This leads to better predictions and decisions based on historical data.

Another advantage of LSTM networks is their ability to be equipped with many layers. Such multilayer networks are extremely adaptive and can solve complex problems that cannot be handled by conventional neural networks.

Feedforward networksQuick and easy to trainNo consideration of time series data
Recurrent networksConsideration of sequences possibleProblems with the processing of long sequences
LSTMBetter processing of long sequencesLonger training times compared to other techniques


Long Short-Term Memory is an important technology in Artificial Intelligence. It enables RNNs to be trained better and thus achieve better performance. The applications of LSTM are diverse and range from speech recognition to anomaly detection in network traffic. Compared to other techniques, LSTM provides better processing of long sequences, but training times can be longer.

Maximilian Schneider Avatar

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