concept
BP neural network (Backpropagation Neural Network) is a common artificial neural network that uses the backpropagation algorithm to train the network and adjust the connection weights to minimize the error between the predicted output and the actual output. This network structure contains input layer, hidden layer and output layer, and uses gradient descent algorithm to optimize the weights.
structure:
BP neural network (Backpropagation Neural Network) is a feedforward neural network with a multi-layer structure. It learns the mapping relationship between input and output by continuously adjusting weights. The structure of BP neural network is introduced in detail below:
1. Input Layer
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Description: The input layer is the first layer of the network and is responsible for receiving raw data or features.
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Node: Each input feature corresponds to an input layer node.
2. Hidden Layers
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Description: The hidden layer performs complex calculations and feature extraction after the input layer. There can be one or more hidden layers.
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Nodes: Each hidden layer contains multiple nodes, and each node is connected to all nodes in the previous layer.
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Activation function: Each hidden layer node applies an activation function, usually a nonlinear function such as Sigmoid, ReLU, etc., to introduce nonlinear factors.