报告题目一：模型不确定性下的状态估计：博弈论和数据驱动方法
报告人：Mattia Zorzi
报告时间：2026年9月7日 9:30
报告地点：正阳楼3号楼102
报告简介：经典的卡尔曼滤波，虽已广泛应用于导航、机器人、金融以及环境监测等领域，然而该方法通常依赖于精确的状态空间模型信息。事实上，在许多实际场景中，系统模型的信息往往只能近似获得，并且不可避免地受到各种不确定性因素的影响。显然，在这种情况下，经典的滤波方法会使估计性能有所下降，甚至出现系统不稳定现象。本次报告将介绍一种基于极小极大博弈论方法的鲁棒状态估计框架。该方法将模型不确定性视为一个“对抗参与者”，并通过设计估计器，使其在最不利情形下的估计误差最小化，从而提高状态估计对模型不确定性的鲁棒性。随后，报告将进一步讨论该方法向非线性系统的推广。最后，报告还将探讨如何利用观测数据学习系统的不确定性信息，进而建立针对最坏情形下的鲁棒设计与数据驱动自适应方法之间的联系。
报告题目二：A second-order generalization of TC and DC kernels
报告人：Mattia Zorzi
报告时间：2026年9月9日 9:30
报告地点：正阳楼3号楼202
报告简介：Kernel-based methods have been successfully introduced in system identification to estimate the impulse response of a linear system. Adopting the Bayesian viewpoint, the impulse response is modeled as a zero mean Gaussian process whose covariance function (kernel) isestimated from the data. The most popular kernels used in system identification are the tuned-correlated (TC), the diagonal-correlated (DC) and the stable spline (SS) kernel. TC and DC kernels admit a closed-form factorization of the inverse. The SS kernel induces more smoothness than TC and DC on the estimated impulse response, however, the aforementioned property does not hold in this case. In this talk we propose a second-order extension of the TC and DC kernel, which induces more smoothness than TC and DC, respectively, on the impulse response and ageneralized-correlated kernel, which incorporates the TC and DC kernels and their second order extensions. Moreover, these generalizations admit a closed-form factorization of the inverse and thus they allow to design efficient algorithms for the search of the optimal kernel hyperparameters. We also show how to use this idea to develop higher oder extensions. Interestingly, these new kernels belong to the family of the so called exponentially convex local stationary kernels: such a property allows to immediately analyze the frequency properties induced on the estimated impulse response by these kernels.
报告题目三：Identification of forward models: a nonparametric approach
报告人：Mattia Zorzi
报告时间：2026年9月9日 14:00
报告地点：正阳楼3号楼202
报告简介：In this talk, we present a new kernel-based method for identifying the impulse responses of forward (or simulation) models from input-output data. While traditional regularized methods re-parameterize systems via one-step ahead predictors, they make it remarkably difficult to encode crucial prior information -- such as stability -- directly into the forward model. To overcome this limitation, we frame the problem directly in terms of the forward model's impulse responses, leading to a nonlinear, infinite-dimensional Tikhonov regularization problem. We prove the existence of a solution, generalize the classical representer theorem to characterize its structure, and show that this justifies approximating the foward system via a high-order MAX (Moving Average with eXogenous input) model. Lastly, we address the problem to tune the kernel hyperparameters from data.
报告人简介：Mattia Zorzi received the M.S. degree in Automation Engineering and the Ph.D. degree in Information Engineering from the University of Padova, Padova, Italy, in 2009 and 2013, respectively. He held Postdoctoral appointments with the Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium, and with the Human Inspired Technology Research Centre, University of Padova, Padova, Italy. He held visiting positions with the Department of Electrical and Computer Engineering, University of California, Davis, USA, and with the Department of Engineering, University of Cambridge, Cambridge, U.K., in 2011 and 2013-2014, respectively. He is currently an Associate Professor with the Department of Information Engineering, University of Padova. His current research interests include machine learning, deep learning, robust estimation, identification theory.
