Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Series Preface * Preface to the Third Edition * 1 Linear Systems * 2 Nonlinear Systems: Local Theory * 3 Nonlinear Systems: Global Theory * 4 Nonlinear Systems: Bifurcation Theory * References * Index

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Differential Equations and Dynamical Systems

System Identification I

Lectures on Cauchy’s Problem in Linear Partial Differential Equations. By J. Hadamard. Pp. viii+316. 15s.net. 1923. (Per Oxford University Press.)

Survey Kernel methods in system identification, machine learning and function estimation: A survey

http://users.isy.liu.se/en/rt/ljung/learn/regulfir.pdf

On the estimation of transfer functions, regularizations and Gaussian processes-Revisited

Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels suitable for impulse response estimation, and equip the kernel-based regularization method with three features. First, multiple kernels can better capture complicated dynamics than single kernels. Second, the estimation of their weights by maximizing the marginal likelihood favors sparse optimal weights, which enables this method to tackle various structure detection problems, e.g., the sparse dynamic network identification and the segmentation of linear systems. Third, the marginal likelihood maximization problem is a difference of convex programming problem. It is thus possible to find a locally optimal solution efficiently by using a majorization minimization algorithm and an interior point method where the cost of a single interior-point iteration grows linearly in the number of fixed kernels. Monte Carlo simulations show that the locally optimal solutions lead to good performance for randomly generated starting points.

/pdf/system-identification-via-sparse-multiple-kernel-based-2s0ry3d7os.pdf

System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques

https://liu.diva-portal.org/smash/get/diva2:641676/FULLTEXT01.pdf

Implementation of algorithms for tuning parameters in regularized least squares problems in system identification

System identification is a mature research area with well established paradigms, mostly based on classical statistical methods. Recently, there has been considerable interest in so called k...

https://www.tandfonline.com/doi/pdf/10.1080/00207179.2019.1578407

Tianshi Chen

Papers

Survey Kernel methods in system identification, machine learning and function estimation: A survey

On the estimation of transfer functions, regularizations and Gaussian processes-Revisited

System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques

Implementation of algorithms for tuning parameters in regularized least squares problems in system identification

A shift in paradigm for system identification