The training process of Back Propagation Neural Network (BPNN) is easily converged at a local minimum

which slows the training process sharply.In this paper

an analysis is given to the chief formative reason of local minimum

and an improved Genetic Algorithm (GA) is introduced to overcome local minimum.Most BPNNs use Sigmoid function as the transfer function of network nodes

this paper indicates that the flat characteristic of Sigmoid function results in the formation of local minimum.In the improved GA

pertinent modifications are made to the evaluation function and the mutation model.The evaluation of solution is associated with both values of error function and gradient model corresponding to the certain solution

so that solutions away from local minimum are highly evaluated.The sensitivity of error function to network parameter is imported to form a self-adapting mutation model

which is powerful to diminish error function.Both modifications help to drive solutions out of local minimum.A case study of a real industrial process shows the advantage of the improved GA to overcome local minimum and to accelerate the training process.