Neural Network Basic Principles; Artificial Neural Network; Long Short-Term Memory Recurrent Neural Network (Lstm Rnn); Figure 2. Artificial Neural Network - ST AEKD-AICAR1 User Manual

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Neural network basic principles

1.1

Artificial neural network

The artificial neural network works as the human brain neural network. Data are transferred to the neuron through
the inputs. Then, they are sent as an output after processing.
Artificial neural networks are built on layers of different neural units. Each unit consists of three parts:
• an input part that receives the data
• a hidden part that uses the neuron weight to calculate the result
• an output part that receives the calculation results and applies an eventual bias
The weight associated with each neuron determines its firing probability. The bias is the measure of assumption
made by the form of the output.
This architecture is typical for deep learning processes like data classification and pattern recognition neural
networks.
1.2

Long short-term memory recurrent neural network (LSTM RNN)

Traditional artificial neural networks keep no memory of what happened in the past. They take their decision only
on the data provided instant by instant. These architectures are well suited for data classification and pattern
recognition.
For other applications, like speech recognition, the proper classification requires a memory of the context, that is,
the prior words in the speech recognition application.
A recurrent neural network (RNN) is a class of artificial neural network that includes neurons connected in a loop.
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Figure 2. Artificial neural network
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Neural network basic principles
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