论线性系统的复杂性：一种通过率失真理论和仿真系统的方法
On the complexity of linear systems: an approach via rate distortion theory and emulating systems
AI总结：
本文利用高斯不确定性源的率失真函数定义并计算连续时间线性系统的复杂性，结合仿真系统端点映射提出源码构造方法，服务于注意力变化控制器设计与未知系统数据驱动分析。
摘要：
我们将连续时间线性系统的复杂性定义为：以期望保真度描述其前向增量所需的最小比特数，并利用这些增量中高斯不确定性源的率失真函数来计算该量。线性系统的复杂性与控制-通信场景相关，这类场景需要基于采样数据表示进行局部和动态决策。我们将这一复杂性概念与注意力变化控制器的设计联系起来，并展示了一种通过所谓仿真系统的端点映射构造源码的新方法，该方法具有对未知动力系统进行非参数、基于数据的仿真与分析的潜力。
英文摘要：
We define the complexity of a continuous-time linear system to be the minimum number of bits required to describe its forward increments to a desired level of fidelity, and compute this quantity using the rate distortion function of a Gaussian source of uncertainty in those increments. The complexity of a linear system has relevance in control-communications contexts requiring local and dynamic decision-making based on sampled data representations. We relate this notion of complexity to the design of attention-varying controllers, and demonstrate a novel methodology for constructing source codes via the endpoint maps of so-called emulating systems, with potential for non-parametric, data-based simulation and analysis of unknown dynamical systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
