Recursive Identification Based on Local Likelihood Function With Binary-Valued Observations

Xin Li

doi:10.1109/TAC.2025.3649296

2025-12-30

PRE

DOI：10.1109/TAC.2025.3649296

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摘要

En 中文

本文研究了有限脉冲响应系统的面向控制的递推辨识问题，观测值为二元取值。受最大似然法的启发，提出了一种基于系统噪声和观测统计特性的新型递推算法。与现有研究不同，所提算法的梯度由局部似然函数推导得到，此前尚未被考虑。该算法的核心优势在于递推权重项的自适应，特别是在估计值偏离真实值较远时具有加速效果。此外，与基于时变阈值的现有算法相比，所提算法通过加权使其适用于固定阈值场景，从而避免了时变阈值带来的复杂性。证明了所提算法在几乎必然和均方意义下均收敛，并在一定条件下获得了几乎必然和均方收敛速率。通过两个仿真验证了所提算法的有效性，并展示了其收敛速率优于现有算法的优势。

Keyword:

Binary-valued observations

likelihood function

stochastic approximation

system identification

期刊

IEEE Transactions on Automatic Control

IF:

论文数:

1.3W

被引数:

6.7W

机构

zhongyuan university of technology

学者数:

1.0K

论文数: 306

被引数: 0

chinese academy of sciences

学者数:

56.7W

论文数: 45.0W

被引数: 704

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