实现一个智能城市,使用基于野木的物联网与基于分数顺序的黄金搜索算法相结合
1Hangzhou Polytechnic, Hangzhou, 311402, Zhejiang, China.
Scientific reports
|November 26, 2024
概括
本研究介绍了针对智能城市数据的优化机器学习框架. 它显著降低了延迟和能源消耗,同时提高了交通管理系统的吞吐量.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 智慧城市技术 智慧城市技术
背景情况:
- 城市地区物联网 (IoT) 设备的快速增长产生了庞大而复杂的数据.
- 有效的数据分析和机器学习对于智能城市的好处至关重要.
- 超参数调整是物联网支持的智能城市中机器学习的一个关键但复杂的挑战.
研究的目的:
- 开发一种创新的优化框架,用于在支持物联网的智能城市系统中进行超参数调整.
- 通过改进机器学习模型,提高智能城市应用程序的效率,可靠性和可持续性.
主要方法:
- 将WildWood算法与分数顺序的金色搜索算法的集成用于超参数优化.
- 在智能城市交通管理场景中基于模拟的评估.
主要成果:
- 实现了高达30%的延迟减少和25%的能源消耗减少.
- 与传统方法相比,系统吞吐量提高了多达20%.
- 优化的WildWood模型在流量预测中产生了0.85的平均平方误差 (MSE).
结论:
- 拟议的优化框架有效地提高了支持物联网的智能城市系统的性能.
- 整合WildWood和分数顺序金色搜索提供了一个强大的解决方案,用于超参数优化.
- 结果证实了框架能够提高效率,减少资源消耗,提高可靠性.
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