通过开发的非洲优化算法-埃尔曼神经网络预测自压缩混凝土的压力强度
Shaoqiang Guo1, Honggang Kou2, Yuzhang Bi3
1Civil Engineering College of Xijing University, Xi'an, 710000, Shaanxi, China.
Scientific reports
|August 29, 2024
概括
预测混凝土的压力强度是一个挑战. 这项研究使用了开发的非洲优化算法 (DAVOA) -Elman神经网络 (ENN) 以更多的参数来准确预测混凝土强度.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 预测混凝土的压力强度是复杂的,因为有很多影响因素.
- 传统的方法与影响具体行为的各种参数作斗争.
- 人工神经网络 (ANN) 为复杂的工程问题提供先进的建模能力.
研究的目的:
- 评估开发的非洲优化算法 (DAVOA) -埃尔曼神经网络 (ENN) 的性能,用于预测自压缩混凝土的压力强度.
- 在混凝土强度预测中确定ENN输入参数和转移函数的最佳数量.
- 评估DAVOA在优化ENN以实现可靠的混凝土财产预测方面的有效性.
主要方法:
- 使用的埃尔曼神经网络 (ENN) 通过非洲优化算法 (DAVOA) 得到了改进.
- 使用8个输入参数对140个输入参数进行ENN性能比较.
- 确定了ENNs的最佳层间传递函数 (Logsig-Purelin).
主要成果:
- 用DAVOA增强的ENN在预测7天和28天压力强度时表现出最小的平均平方测试误差.
- 洛格西格-普雷林转移函数被证明是预测两种压力强度的最佳方法.
- 与8个参数网络相比,具有140个参数的网络在测试错误减少方面显示出显著的改善 (74.54%为7天,70.44%为28天).
结论:
- DAVOA是一个强大的,时间和成本高效的工具,用于优化ENNs在预测混凝土的压力强度.
- 增加输入参数可以显著提高预测准确度并降低错误率.
- 开发的DAVOA-ENNs提供了一个灵活而准确的解决方案,用于预测具体的行为.
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