连续时间随机系统中的信息传输和流量的多样性和相似性
1Institute of Physics, Saratov State University, 155 Moskovskaya Street, Saratov 410012, Russia.
Chaos (Woodbury, N.Y.)
|August 25, 2025
概括
这项研究为连续时间的随机系统引入了新的动态因果效应 (DCE) 量化器. 这些新的措施捕捉了信息流和因果关系,扩展了以前对马尔科夫链的研究.
科学领域:
- 动态系统理论
- 信息理论
- 统计物理
背景情况:
- 现有的因果关系框架通常集中在离散时间或特定的系统类型上.
- 在连续的随机系统中量化信息流存在独特的挑战.
- 以前的工作主要考虑马尔科夫链,限制了对更广泛的动态系统的适用性.
研究的目的:
- 将动态因果效应 (DCE) 的框架扩展到具有连续时间和状态的随机动态系统.
- 引入新的信息 DCE,包括第一和第二级的 DCE 率.
- 建立这些新定义的因果关系量化器之间的分析关系.
主要方法:
- 应用动态因果关系框架.
- 开发具有有限响应时间的信息DCE.
- 介绍和分析第一和第二阶段的DCE率.
- 不同的DCE测量之间的分析关系的推导.
- 使用合随机放松系统进行数值验证.
主要成果:
- 为连续的随机系统生成了十几个信息因果定量器的逻辑序列.
- 引入了具有有限响应时间和相应的DCE率的信息DCE.
- 研究的信息DCE之间建立了分析关系.
- 在新框架内确定了众所周知的信息传输和流量指标,如传输.
- 使用合随机放松系统获得典型的数值和定量关系.
结论:
- 扩展的DCE框架提供了一套全面的工具来量化连续随机动态系统中的因果关系.
- 新推出的DCE比率可以更深入地了解信息流动的动态.
- 这些发现有助于在单一的理论结构中统一理解各种信息传输措施.
相关概念视频
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01:24BIBO stability of continuous and discrete -time systems
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01:22Basic Continuous Time Signals
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01:27Steady Flow of a Fluid Stream
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01:26Feedback control systems
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01:11Sampling Continuous Time Signal
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