Data-driven approximation and reduction from noisy data in matrix pencil frameworks

The Padé matrix pencil method with spurious pole information assimilation

We present a novel method for calculating Padé approximants that is capable of eliminating spurious poles placed at the point of development and of identifying and eliminating spurious poles created by precision limitations and/or noisy coefficients. Information contained in in the eliminated poles is assimilated producing a reduced order Padé approximant (PA). While the [m+k/m] conformation produced by the algorithm is flexible, the m value of the rational approximant produced by the algorithm reported here is determined by the number of spurious poles eliminated. Spurious poles due to coeffi…

基于数据的有正则化的时间受限h2模型降阶

Data-Driven Regularized Time-Limited h2 Model Reduction from Noisy Impulse Responses

本文提出一种基于数据的时间受限h2模型降阶方法，利用噪声冲击响应数据解决正则化问题，实验表明其在噪声环境下表现优于传统方法。

Phase Reduction and Synchronization of Coupled Noisy Oscillators

We study the synchronization behavior of a noisy network in which each system is driven by two sources of state-dependent noise: (1) an intrinsic noise which is common among all systems and can be generated by the environment or any internal fluctuations, and (2) a coupling noise which is generated by interactions with other systems. After providing sufficient conditions that foster synchronization in networks of general noisy systems, we focus on weakly coupled networks of noisy oscillators and, using the first- and second-order phase response curves (PRCs), we derive a reduced order stochast…

从噪声数据到分层控制：一种模型降阶框架

From Noisy Data to Hierarchical Control: A Model-Order-Reduction Framework

本文提出一种直接数据驱动的框架，用于构建具有未知动态和过程扰动的离散时间线性动力系统降阶模型，通过噪声数据设计接口函数实现控制器合成与优化。

数据驱动的非线性系统模型降阶方法

Data-Driven Model Order Reduction of Nonlinear Systems with Noisy Data

本文提出数据驱动框架，用于构建无数学模型的非线性系统降阶模型，通过相似性关系和半定规划条件，实现控制器合成与逻辑规范满足。