基于IPOA-BP的输电塔复合基础极限抗拔承载力预测模型
Prediction model of ultimate pullout capacity of transmission tower composite foundation based on IPOA-BP
杨世强 1李小来 1王彦海 2曹铖 1马立 1尹恒伟2
作者信息
- 1. 国网湖北省电力有限公司超高压公司 武汉 430051
- 2. 三峡大学电气与新能源学院 宜昌 443002
- 折叠
摘要
为了实现输电塔复合基础极限抗拔承载力的准确预测,克服传统理论、经验公式误差大,计算慢的问题,提出一种改进鹈鹕智能算法(IPOA)来优化BP神经网络的承载力预测模型.首先,利用SPM混沌映射、Levy飞行以及融合非线性惯性权重因子ω的正余弦优化策略,对鹈鹕优化算法(POA)改进;然后,利用IPOA对BP神经网络的权值和阈值参数寻优,得到IPOA-BP预测模型;最后,基于验证后的数值试验构建数据集,对IPOA-BP预测模型进行训练和测试.结果表明,IPOA-BP与POA-BP预测模型相比,方根误差下降65.75%,绝对平均误差下降65.79%,平均相对误差下降65.60%,可见IPOA-BP神经网络能够实现复合基础抗拔承载力较准确的预测,为该类型基础的承载力预测提供了新方法.
Abstract
In order to achieve accurate prediction of the ultimate elevation bearing capacity of the composite foundation of transmission towers and overcome the problems of large error and slow calculation of the theoretical or traditional empirical formulas,an improved pelican intelligent algorithm(IPOA)is proposed to optimize the bearing capacity prediction model of the BP neural network.Firstly,the pelican optimization algorithm(POA)is optimized using SPM chaotic mapping,Levy flight,and a positive cosine optimization strategy that incorporates nonlinear inertial weight factor ω.Then,the optimized IPOA is used to find the optimization of the weight and threshold parameters of the BP neural network,and the IPOA-BP prediction model is obtained;finally,a dataset is constructed based on validated simulation experiments and the IPOA-BP prediction model is trained and tested.The results show that compare with the POA-BP prediction model,the square root error of IPOA-BP decreases by 65.75%,the absolute average error decreases by 65.79%,and the average relative error decreases by 65.60%,it can be seen that IPOA-BP neural network can achieve a more accurate prediction of the composite foundation's resistance to elevation bearing capacity,which provides a new method for the prediction of the bearing capacity of this type of foundation.
关键词
改进鹈鹕优化算法/复合基础/BP神经网络/SPM混沌映射/正余弦优化策略Key words
improved pelican optimization algorithm/composite foundation/BP neural network/SPM chaotic mapping/positive cosine optimization strategy引用本文复制引用
基金项目
国家自然科学基金(U22A20600)
国家自然科学基金(52079070)
出版年
2024
NETL
NSTL
万方数据