**2.1 The connotation of neural network**

The neural network model is used to simulate the process of a large number of neurons in the human brain, including information processing, processing, storage, and search. Its main features include (1) the characteristics of distributed storage of information. (2) Information processing and reasoning have the characteristics of parallelism. (3) Information processing has the characteristics of self-organization and self-learning. (4) It has a very strong non-linear mapping capability from input to output [7].

The topology of the neural network connection method is a graph with neurons as nodes and directed connections between nodes as edges. The structure can be divided into two categories: layered and gridded. A neural network with a hierarchical structure consists of several layers. Each layer has a certain number of neurons. Neurons in adjacent layers are unidirectional connected. Normally, neurons in the same layer cannot connect. In a neural network with a network structure, any two neurons may be connected in both directions. The following are several common neural network structures including (1) forward neural networks. (2) Feedback neural network. (3) Integrate neural networks with each other. (4) Hybrid neural network.

Neurons in the human brain pass through the fine structures of many dendrites, collect information from other neurons, and burst electrical activity pulses through the axis. How to adjust the connection weight is reconstructed into different learning algorithms. In order to apply neural networks to solve practical engineering problems, they must be trained. This is neural network teacher learning or

supervised learning. And neural network learning usually refers to unsupervised learning of neural networks. In addition, after training the neural network through the sample data set, when new data other than the sample data set appears in the input, the neural network can still obtain new outputs through learning, and can strictly maintain the input–output mapping relationship after the input. The training ability of neural networks is called the generalization ability of neural networks. By changing the structure and parameters of the neural network, you can change the size of the network to make it more suitable for solving specific problems. This process is called the growth and pruning of neural networks [8].
