复杂网络的区域型集合成员融合估计:一个缓冲辅助策略
IEEE transactions on cybernetics
|November 4, 2025
概括
这项研究引入了复杂网络 (CN) 中区域组成员融合估计 (SMFE) 的新算法. 这些方法提高了间歇传输测量数据的利用率,确保了有限的估计不确定性.
科学领域:
- 控制系统工程 控制系统工程
- 网络化系统 网络化系统
- 估计理论 估计理论
背景情况:
- 复杂网络 (CNs) 面临的挑战是通过共享网络传输测量,因为带宽限制和间歇性数据.
- 现有的方法与数据丢失和可变的传输间隔作斗争,影响估计的准确性.
研究的目的:
- 为具有间歇性数据传输的复杂网络 (CNs) 设计区域组成员融合估计 (SMFE) 算法.
- 为了提高使用缓冲数据的测量利用率,并解决超过采样时间的传输间隔.
- 确保网络系统中估计不确定性的局限性.
主要方法:
- 建议采用批处理方法,通过代CN的状态方程,同时处理输入数据.
- 在设计SMFE算法时,采用了区域特征集成员估计 (Zonotopic set-membership estimation,SME) 技术.
- 建立了足够的标准,以保证输出zonotope大小的统一界限.
主要成果:
- 为使用缓冲,间歇传输数据的CN开发了新的SMFE算法.
- 拟议的算法有效地处理比采样时间更长的传输间隔.
- 建立了对均界限的区域型大小的保证,以确保估计稳定性.
结论:
- 开发的SMFE算法在具有通信限制的复杂网络中提高了数据利用和估计性能.
- 这些发现为间歇连接系统的状态估计提供了强大的框架.
- 数字示例验证了拟议的算法的有效性和实际适用性.
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