The Application of the Improved BP Neural Network in Prediction of Pipeline Corrosion Rate

The BP shortcomings are apt to fall into some minimum, slowing to converge and causing the effect of shaking, influencing its application to the prediction of the pipeline corrosion rate. According to the characteristic of the improved adaptive GA—EIAGA in extensive space search and converging to the optimum goal as soon as possible in the optimum direction, this paper points out that the improved adaptive GA—IAGA should be used to optimize the BP neural network and structure the optimized mix algorithm neural network model. The application of the optimized model to the pipeline corrosion rate prediction shows that it will prove greatly the learning effectively and the accuracy in prediction and judging.