时间：9月9日（周三）14:00
地点：正阳楼3号楼202
主讲人：Mattia Zorzi
摘要：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 forward system via a high-order MAX (Moving Average with eXogenous input) model. Lastly, we address the problem to tune the kernel hyperparameters from data.
