.png){kind=icon}
返回
Recursive Network Estimation for a Model With Binary-Valued States
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2022.3195268.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article studies how to estimate the weighted adjacency matrix of a network out of the state sequence of a model with binary-valued states, by using a recursive algorithm. In the considered system, agents display and exchange these binary-valued states generated from intrinsic quantizers. It is shown that stability of the model and identifiability of the system parameters can be guaranteed under continuous random noise. Under standard Gaussian noise, the problem of estimating the real-valued weighted adjacency matrix and the unknown quantization threshold is transformed to an optimization problem via a maximum likelihood approach. It is further verified that the unique solution of the optimization problem is the true parameter vector. A recursive algorithm for the estimation problem is then proposed based on stochastic approximation techniques. Its strong consistency is established and convergence rate analyzed. Numerical simulations are provided to illustrate developed results.
Keyword:
Binary-valued states
identifiability
network estimation
quantized identification
stochastic approximation (SA)
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
Analytical Solution of Interface Shear Stresses in Externally Bonded FRP-Strengthened Concrete Beams
PREAI
分享收藏
分享收藏Recursive identification of systems with binary-valued outputs and with ARMA noises.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏分享收藏
查看更多内容