足立研セミナー: Outlier-robust estimation of uncertain-input systems with applications to nonparametric FIR and Hammerstein models
Prof Hakan Hjalmarsson（KTH（スウェーデン王立工科大学））
- 2019年1月18日（金） 16:30～17:30
In this paper, we present the class of uncertain-input models, and extend it to handle cases of measurements with outliers. The general uncertain-input model framework allows us to treat system identification problems in which a linear system, represented by its impulse response, is subject to an input about which we have partial information. Both the impulse response and the input are modeled as Gaussian processes and the kernels are used to encode the information available. The whole model is then estimated using an approximate empirical Bayes approach. We extend the uncertain-input model framework to non-Gaussian measurement models by considering the noise precisions as realizations of a Gamma prior. This is joint work with Dr Riccardo Risuleo.
Copyright © Adachi Lab. All rights reserved.