The influencing factors of pipeline corrosion are many and complex, so establishing an accurate prediction model of pipeline corrosion rate is the key to evaluate pipeline safety. Aiming at the defects of the traditional Elman Neural Network model, which is easy to fall into the minimum value and has weak generalization ability, a new ENN model based on improved reptile search algorithm (introducing Circle chaos map and combined hunting strategy with whale optimization algorithm) is proposed, and the effectiveness of the new model is verified by the measured results of pipeline corrosion rate. The prediction results of two examples show that the mean absolute percentage error of the improved new model is 0.5476% and 0.7831% respectively, and its prediction accuracy is obviously higher than that of the traditional ENN model. The new model can optimize the weights and thresholds in the prediction process, so it is helpful to improve the prediction accuracy of the traditional model.
卢鹏飞,王霄,杨文博,等. 改进爬行动物搜索算法优化ENN模型预测管道腐蚀速率[J]. 科学技术与工程, 2023, 23(30): 12942-12950.
Lu Pengfei, Wang Xiao, Yang Wenbo, et al. Improved reptile search algorithm to optimize ENN model to predict pipe corrosion rate[J]. Science Technology and Engineering,2023,23(30):12942-12950.