Pole Recovery From Noisy Data on Imaginary Axis
نویسندگان
چکیده
This note proposes an algorithm for identifying the poles and residues of a meromorphic function from its noisy values on imaginary axis. The uses Möbius transform Prony’s method in frequency domain. Numerical results are provided to demonstrate performance algorithm.
منابع مشابه
Recovery of Blocky Images from Noisy and Blurred Data
The purpose of this investigation is to understand situations under which an enhancement method succeeds in recovering an image from data which are noisy and blurred. The method in question is due to Rudin and Osher. The method selects, from a class of feasible images, one that has the least total variation. Our investigation is limited to images which have small total variation. We call such i...
متن کاملSystem Identification Based on Frequency Response Noisy Data
In this paper, a new algorithm for system identification based on frequency response is presented. In this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of...
متن کاملSystem Identification Based on Frequency Response Noisy Data
In this paper, a new algorithm for system identification based on frequency response is presented. In this method, given a set of magnitudes and phases of the system transfer function in a set of discrete frequencies, a system of linear equations is derived which has a unique and exact solution for the coefficients of the transfer function provided that the data is noise-free and the degrees of...
متن کاملLow-Shot Learning from Imaginary Data
Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses thi...
متن کاملBinary Graph-Signal Recovery from Noisy Samples
We study the problem of recovering a smooth graph signal from incomplete noisy measurements, using random sampling to choose from a subset of graph nodes. The signal recovery is formulated as a convex optimization problem. The optimality conditions form a system of linear equations which is solvable via Laplacian solvers. In particular, we use an incomplete Cholesky factorization conjugate grad...
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: Journal of Scientific Computing
سال: 2022
ISSN: ['1573-7691', '0885-7474']
DOI: https://doi.org/10.1007/s10915-022-01963-z