1. About neural networks
1.1 What is an artificial neural network
Artificial Neural Networks (ANNs) are also called Neural Networks (NNs) or Connection Models for short. It is a kind of algorithmic mathematics that imitates the behavioral characteristics of animal neural networks and performs distributed parallel information processing. model. This kind of network relies on the complexity of the system and achieves the purpose of processing information by adjusting the interconnection between a large number of internal nodes.
In machine learning and related fields, the computational models of artificial neural networks (artificial neural networks) are inspired by the central nervous system of animals (especially the brain), and are used to estimate or can rely on a large number of inputs and general unknown approximate functions. Artificial neural networks are usually presented as interconnected "neurons", which can calculate values from input, and are capable of machine learning and pattern recognition systems due to their adaptive nature.
1.2 What is BP neural network
BP (back propagation) neural network is a concept proposed by scientists led by Rumelhart and McClelland in 1986. It is a multi-layer feedforward neural network trained according to the error back propagation algorithm and is currently the most widely used neural network. The BP network adds several layers (one or more layers) of neurons between the input layer and the output layer. These neurons are called hidden units. They have no direct connection with the outside world, but their state changes can affect the input. The relationship with the output, each layer can have several nodes.
1.3 Analysis of the use of neural networks
2. Derivation of BP neural network
2.1 What is a neural network
2.2 What is BP neural network
2.3 Analysis of the use of neural networks
| version number | date | Author |
|---|---|---|
| V0.1 | March 6, 2018 | Lei Xiaoman |