26 citations · 41 across the 16 of their papers we have counts for
16 papers
Sequentially decoupling estimators for Box-Jenkins model estimation
Biqiang Mu
In this paper, we propose a consistent and asymptotically efficient estimation method for Box-Jenkins (BJ) models that is applicable under both open-loop and closed-loop data condi…
On Kernel Design for Regularized Volterra Series Identification of Wiener-Hammerstein Systems
Yu Xu, Biqiang Mu, Tianshi Chen
There have been increasing interests on the Volterra series identification with the kernel-based regularization method. The major difficulties are on the kernel design and efficien…
Kernel-Based Regularized Continuous-Time System Identification from Sampled Data
Xiaozhu Fang, Biqiang Mu, Tianshi Chen
The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century.…
Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems
Xian Yu, Xiaozhu Fang, Biqiang Mu +1
For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen mo…
Consistent and Asymptotically Efficient Localization from Range-Difference Measurements
Guangyang Zeng, Biqiang Mu, Ling Shi +2
We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee t…
On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification
Biqiang Mu, Tianshi Chen, He Kong +3
Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error meth…
An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications
Junpeng Zhang, Yue Ju, Biqiang Mu +2
Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization o…
Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs
He Kong, Salah Sukkarieh, Travis J. Arnold +3
Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises i…
Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator
Yue Ju, Biqiang Mu, Lennart Ljung +1
Regularized system identification is the major advance in system identification in the last decade. Although many promising results have been achieved, it is far from complete and…
Global and Asymptotically Efficient Localization from Range Measurements
Guangyang Zeng, Biqiang Mu, Jiming Chen +2
We consider the range-based localization problem, which involves estimating an object's position by using sensors, hoping that as the number of sensors increases, the estim…
Tutorial on Asymptotic Properties of Regularized Least Squares Estimator for Finite Impulse Response Model
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we give a tutorial on asymptotic properties of the Least Square (LS) and Regularized Least Squares (RLS) estimators for the finite impulse response model with filter…
Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms
Biqiang Mu, Tianshi Chen, Changming Cheng +1
This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker conditio…
On the Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Regularized Least Squares Regression Problems
Biqiang Mu, Tianshi Chen, Lennart Ljung
The asymptotic optimality (a.o.) of various hyper-parameter estimators with different optimality criteria has been studied in the literature for regularized least squares regressio…
Supplementary Material for CDC Submission No. 1461
Yue Ju, Tianshi Chen, Biqiang Mu +1
In this paper, we focus on the influences of the condition number of the regression matrix upon the comparison between two hyper-parameter estimation methods: the empirical Bayes (…
On Input Design for Regularized LTI System Identification: Power-constrained Input
Biqiang Mu, Tianshi Chen
Input design is an important issue for classical system identification methods but has not been investigated for the kernel-based regularization method (KRM) until very recently. I…
On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods
Biqiang Mu, Tianshi Chen, Lennart Ljung
The kernel-based regularization method has two core issues: kernel design and hyperparameter estimation. In this paper, we focus on the second issue and study the properties of sev…