饥饿游戏搜索和物理引导的人工神经网络的基于曲的新机制的行为建模
Hieu Giang Le1, Thanh-Phong Dao1, Minh Phung Dang1
1Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam.
Scientific reports
|January 15, 2025
概括
这项研究引入了一种新的方法,用于使用饥饿游戏搜索和物理引导的人工神经网络对合规机制建模. 这种方法提高了准确性和融合速度,克服了传统技术的局限性.
科学领域:
- 机械工程 机械工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 合规机制对于精确定位系统至关重要,但面临着像不稳定的结果和有限的训练数据这样的建模挑战.
- 现有的研究尚未探索用于优化神经网络参数在合规机制建模中的元启发学.
- 物理引导的人工神经网络 (Phy-GANNs) 是有前途的,但尚未应用于符合规定的机制.
研究的目的:
- 开发一种用于建模符合机制行为的新方法.
- 解决现有建模技术的局限性,包括不稳定的结果和数据依赖性.
- 将元启发学优化与先进的神经网络架构集成在一起,以提高性能.
主要方法:
- 这项研究开创了Hunger Game Search (HGS) 算法的使用,以优化神经网络的权重和偏差.
- 使用物理引导人工神经网络 (Phy-GANNs),将物理原理纳入神经网络模型.
- 将目标函数最小化,利用物理洞察力和数据驱动信息.
主要成果:
- 饥饿游戏搜索在反向传播方法上表现出优异的性能,产生较小的建模错误.
- 在各种训练集比率和ANOVA测试中进行的调查证实了HGS的稳定性.
- 与Phy-GANNs集成HGS可以显著减少错误并加速与传统神经网络相比的融合.
结论:
- 使用饥饿游戏搜索和物理引导人工神经网络的拟议方法为合规机制建模提供了强有力的解决方案.
- 这种方法克服了以前的局限性,提供了更稳定,更准确的结果.
- 这些发现突显了HGS-Phy-GANNs在推进符合规范的机制设计和应用方面的潜力.
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