山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (6): 8-15.doi: 10.6040/j.issn.1672-3961.0.2023.156
李源1,2,张妮1,张艳娜1,2,刘士豪1,李学辉3
LI Yuan1,2, ZHANG Ni1, ZHANG Yanna1,2, LIU Shihao1, LI Xuehui3
摘要： 为解决传统奇异积分计算方案复杂、计算成本高昂的问题,提出一种构建神经网络代理模型的方法,用于在线阶段预测边界元弱奇异积分结果。从理论上探讨使用机器学习方法预测边界元奇异积分的可行性,利用离线阶段收集的边界元弱奇异积分数据作为训练样本,通过坐标空间转换法解决训练空间和预测空间不一致的问题;引入灰狼等级制位置更新方法和自适应优化策略,提出一种改进的自适应樽海鞘(improved adaptive salp swarm algorithm, IASSA)优化方法,提高神经网络模型预测精度,解决标准樽海鞘优化算法收敛速度慢、后期种群多样性差的问题。试验结果表明,基于IASSA优化的神经网络代理模型能够将预测精度提高约54.50%,IASSA的收敛速度比标准樽海鞘优化算法提高约66.67%,降低了离线阶段代理模型的训练时间。
中图分类号:
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