一个通用的连贯性框架,用于量化多输入系统中的输入贡献,其中有相关或无相关的输入
Nolan H Howes1, Matthew S Allen1, Dario Farina2
1Mechanical Engineering, Brigham Young University, 350 EB, Provo, Utah 84602.
Biomedical signal processing and control
|March 3, 2025
概括
新的连贯性测量准确量化复杂系统中的个体输入贡献,即使与相关输入. 这种进步克服了现有的强大系统分析方法的局限性.
科学领域:
- 信号处理 信号处理
- 系统分析,系统分析.
- 生物医学工程 生物医学工程
背景情况:
- 在多输入系统中,量化输入贡献至关重要.
- 现有的连贯性措施在相关输入时失败.
- 准确的贡献分析需要处理输入相关性.
研究的目的:
- 在任何多输入系统中开发一个通用的连贯性框架,用于准确的贡献分析.
- 引入解决输入相关性的新连贯性措施.
- 通过现有方法和现实数据来验证新的措施.
主要方法:
- 定义了三个新的连贯性措施:组成部分,排除和孤立的连贯性.
- 将输出功率分解为直接输入贡献和干扰.
- 模拟了一个具有不同关联级别的三输入系统.
- 应用措施对一个关于基本震的案例研究.
主要成果:
- 排除和孤立的连贯性准确地估计了所有相关水平的真实贡献.
- 与现有方法相比,新测量结果显示随机误差减少.
- 组件连贯性提供了输出功率的中间分解.
结论:
- 拟议的扩展一致性框架使得无论输入相关性如何,都能够进行准确的贡献分析.
- 排除和孤立的连贯性是多输入系统的可靠措施.
- 这些新型措施将现有的连贯性分析泛化,提高准确性并减少错误.
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