On kernel design for regularized LTI system identification
نویسندگان
چکیده
منابع مشابه
On kernel design for regularized LTI system identification
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 identified, and the other one is how to tune the kernel such that the resulting regularized impulse response estimator can achieve a good bias-variance tradeoff. In this paper, we focus on the issue of kernel design. Depen...
متن کاملOn Input Design for Regularized LTI System Identification: Power-constrained Input
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. In this paper, we consider in the time domain the input design problem of KRMs for LTI system identification. Different from the recent result, we adopt a Bayesian perspective and in particular make use of scalar measu...
متن کاملLti System Identification Using Wavelets
We describe the use of the discrete wavelet transform (DWT) for system identification. Identification is achieved by using a test excitation to the system under test (SUT) that also acts as the analyzing function for the DWT of the SUT’s output, so as to recover the impulse response. The method uses as excitation any signal that gives an orthogonal inner product in the DWT at some step size (th...
متن کاملsimulation and design of electronic processing circuit for restaurants e-procurement system
the poor orientation of the restaurants toward the information technology has yet many unsolved issues in regards to the customers. one of these problems which lead the appeal list of later, and have a negative impact on the prestige of the restaurant is the case when the later does not respond on time to the customers’ needs, and which causes their dissatisfaction. this issue is really sensiti...
15 صفحه اولMulti-kernel regularized classifiers
A family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve multi-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these multi-kernel regularized classifiers. The error analysis consists of two parts: regularization error and sample error. Allowin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Automatica
سال: 2018
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2017.12.039