预测误差方法用于动态系统子网络辨识
Prediction Error Method for Identification of Subnetworks of Dynamical Systems
浏览论文内容
中文总结 AI 辅助
本文提出预测误差方法辨识动态网络中的子网络，通过附加边界信号并扩大子网络满足一致性条件，在稳定性等假设下获得传递函数的一致估计，且预测器非线性允许更少内部测量。
中文摘要 AI 辅助
本文考虑动态网络中嵌入子网络的预测误差辨识问题。进入子网络的测量边界信号被附加到外生输入中。一致性要求这些边界信号中由子网络生成的部分可从测量历史中重构；子网络可被扩大以获得简单的结构充分条件。在所陈述的模型、稳定性、延迟、扰动分离、信息性和可辨识性假设下，所得准则产生子网络传递函数的一致估计。预测器可非线性地依赖于开环传递函数，从而允许在比基于这些传递函数线性的预测器的方法更少的测量内部信号下获得一致估计。
英文摘要
This paper considers prediction-error identification of a subnetwork embedded in a dynamic network. Measured boundary signals entering the subnetwork are appended to the exogenous inputs. Consistency requires that the part of these boundary signals generated by the subnetwork be reconstructible from the measured history; the subnetwork may be enlarged to obtain a simple structural sufficient condition. Under the stated model, stability, delay, disturbance-separation, informativity, and identifiability assumptions, the resulting criterion yields consistent estimates of the subnetwork transfer functions. The predictor may depend nonlinearly on the open-loop transfer functions, allowing consistent estimation with fewer measured internal signals than methods based on predictors that are linear in those transfer functions.
发表机构
- Linköping University（林雪平大学）
- Lund University（隆德大学）
- Technical University of Denmark（丹麦技术大学）
机构由 AI 辅助整理，请以论文原文为准。
