What is BP neural network?

The basic idea of BP algorithm is that the learning process consists of two parts: signal forward propagation and error backward propagation; In forward propagation, input samples are introduced from the input layer, processed layer by layer by each hidden layer in turn, and transmitted to the output layer. If the output of the output layer does not meet the expectation, the error is sent back layer by layer as an adjustment signal, and the connection weight matrix between neurons is processed to reduce the error. After repeated learning, the error is finally reduced to an acceptable range. The specific steps are as follows:

1. Extract samples from the training set and input the information into the network.

2. The actual output of the neural network is obtained by the forward layer-by-layer processing of the connections between nodes.

3. Calculate the error between the actual output and the expected output of the network.

4. The error is transmitted back to the previous layers layer by layer, and the error signal is loaded on the connection weight according to certain principles, so that the connection weight of the whole neural network is transformed to the direction of error reduction.

5. Repeat the above steps for each input-output sample pair in the training set until the error of the whole training sample set is reduced to meet the requirements.