BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer).

Fortran_bp.for

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