The hierarchical structure of artificial neural network includes

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The hierarchical structure of artificial neural network includes neurons, layers and networks.

1, neuron is the most basic unit of artificial neural network. Cells are grouped by layers, and each neuron in each layer is connected with the neurons in the upper layer and the lower layer. * * * is divided into input layer, output layer and hidden layer, which are connected to form a neural network.

2. The input layer only receives the information of the external environment, and it is composed of input units, which can receive all kinds of characteristic information in the sample. Each neuron in this layer is equivalent to an independent variable, which does not complete any calculation and only transmits information for the next layer; The hidden layer is located between the input layer and the output layer. These layers are completely used for analysis, and their function is to link the variables in the input layer with those in the output layer to make them more suitable for data. Finally, the output layer generates the final result, and each output unit will correspond to a specific classification, which is the result value sent by the network to the external system. The whole network can achieve the purpose of learning by adjusting the link strength program.

3. Neural network is an operation model, which consists of a large number of interconnected nodes (or neurons). Each node represents a specific output function, called an activation function. The connection between every two nodes represents a weighted value of the signal passing through the connection, which is called weight, which is equivalent to the memory of artificial neural network. The output of the network varies according to the connection mode, weight and excitation function of the network.