一个新的间隔类型-2模糊的卡尔曼过和跟踪实验数据的新型间隔
Daiana Caroline Dos Santos Gomes1, Ginalber Luiz de Oliveira Serra2
1Federal University of Maranhão, Av. dos Portugueses, 1966, Vila Bacanga, São Luís, Maranhão CEP: 65080-805 Brazil.
Evolving systems
|April 16, 2024
概括
本研究介绍了一种新的模糊卡尔曼波器设计,使用间隔类型-2模糊模型来改进数据跟踪和预测. 该方法增强了对混乱系统和现实世界的数据 (如COVID-19传播) 的预测.
科学领域:
- 控制系统工程 控制系统工程
- 数据科学数据科学数据科学
- 计算智能是一种计算智能.
背景情况:
- 动态系统的准确建模和预测在各种科学领域至关重要.
- 传统的卡尔曼过器与实验数据固有的不确定性和非线性作斗争.
- 间隔类型-2模糊逻辑为处理系统建模中的不确定性提供了增强的能力.
研究的目的:
- 建议使用间隔类型-2模糊模型设计模糊卡尔曼波器的新方法.
- 用实验数据增强动态系统的跟踪和预测.
- 为了证明过器在杂环境和现实应用中的有效性.
主要方法:
- 局部状态空间线性子模型的递归参数估计.
- 应用一个间隔类型-2模糊的观察者/卡尔曼波器识别 (OKID) 算法.
- 数据分区使用间隔类型-2模糊的古斯塔夫森-凯塞尔集群.
- 更新间隔通过实验数据的递归光谱分解获得卡尔曼增益.
主要成果:
- 提出的方法有效地过和跟踪时间延迟的状态变量陈的混乱的吸引器在一个杂的环境中.
- 计算结果验证了间隔类型-2模糊卡尔曼波器的有效性.
- 实验结果表明适用于适应性,实时预测巴西COVID-19传播动态.
结论:
- 开发的间隔类型-2模糊卡尔曼波器为复杂的动态系统的建模和预测提供了强大的方法.
- 该方法显示了在流行病传播等关键应用中实时适应性预测的巨大潜力.
- 光谱分解集成增强了过器处理不可观察的组件的能力,并提高了准确性.
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