改进爬行动物搜索算法优化ENN模型预测管道腐蚀速率
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TE832

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Improved reptile search algorithm to optimize ENN model to predict pipe corrosion rate
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    摘要:

    管道腐蚀的影响因素多而复杂,建立准确的管道腐蚀速率预测模型是评价管道安全状况的关键。针对传统Elman神经网络(ENN)模型预测中易陷入极小值、泛化能力不强的缺陷,提出了一种基于改进爬虫搜索算法(引入Circle混沌映射并结合鲸鱼优化算法的狩猎策略)的ENN模型,并采用管道腐蚀速率实测结果验证了新模型的有效性。两个实例的预测结果表明,改进新模型的平均绝对百分比误差分别为0.5476%、0.7831%,其预测精度明显高于传统ENN模型。新模型在预测过程中可对权值和阀值进行寻优处理,因此有助于提升传统模型的预测精度。

    Abstract:

    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.

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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.

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  • 收稿日期:2023-02-03
  • 最后修改日期:2023-08-01
  • 录用日期:2023-06-09
  • 在线发布日期: 2023-11-15
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