54 citations · 143 across the 23 of their papers we have counts for
23 papers
On Kernel Design for Regularized Non-Causal System Identification
Xiaozhu Fang, Tianshi Chen
Through one decade's development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging n…
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…
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…
The Noise Covariances of Linear Gaussian Systems with Unknown Inputs Are Not Uniquely Identifiable Using Autocovariance Least-squares
He Kong, Salah Sukkarieh, Travis J. Arnold +2
Existing works in optimal filtering for linear Gaussian systems with arbitrary unknown inputs assume perfect knowledge of the noise covariances in the filter design. This is imprac…
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…
Accelerated Sparse Bayesian Learning via Screening Test and Its Applications
Yiping Jiang, Tianshi Chen
In high-dimensional settings, sparse structures are critical for efficiency in term of memory and computation complexity. For a linear system, to find the sparsest solution provide…
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 (…
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Feng Yin, Lishuo Pan, Xinwei He +4
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However…
Continuous-time DC kernel --- a stable generalized first-order spline kernel
Tianshi Chen
The stable spline (SS) kernel and the diagonal correlated (DC) kernel are two kernels that have been applied and studied extensively for kernel-based regularized LTI system identif…
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…
On kernel design for regularized LTI system identification
Tianshi Chen
There are two key issues for the kernel-based regularization method: one is how to design a suitable kernel to embed in the kernel the prior knowledge of the LTI system to be ident…
Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint
Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso +2
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in linear system identification. In particular, all the ad…
Maximum entropy properties of discrete-time first-order stable spline kernel
Tianshi Chen, Tohid Ardeshiri, Francesca P. Carli +3
The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a ze…
Regularized system identification using orthonormal basis functions
Tianshi Chen, Lennart Ljung
Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal ba…
Maximum Entropy Kernels for System Identification
Francesca Paola Carli, Tianshi Chen, Lennart Ljung
A new nonparametric approach for system identification has been recently proposed where the impulse response is modeled as the realization of a zero-mean Gaussian process whose cov…
Maximum Entropy Property of Discrete-time Stable Spline Kernel
Tohid Ardeshiri, Tianshi Chen
In this paper, the maximum entropy property of the discrete-time first-order stable spline kernel is studied. The advantages of studying this property in discrete-time domain inste…
Scalable Anomaly Detection in Large Homogenous Populations
Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad +2
Anomaly detection in large populations is a challenging but highly relevant problem. The problem is essentially a multi-hypothesis problem, with a hypothesis for every division of…