BP (Back Propagation) network is a group of scientists led by Rumelhart in 1986 and McCelland, is a kind of error back propagation training algorithm for the multilayer feedforward network, the neural network model is one of the most widely used. BP networks can learn and store a large number of mapping relations between input and output patterns without revealing mathematical equations describing such mappings. Its learning rule is to use the steepest descent method (gradient method) to continuously adjust the weights and thresholds of the network by back-propagation, so that the error sum of the network is minimum. BP neural network model topology includes input layer (input, layer), hidden layer (hide, layer) and output layer (output layer).

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