On adaptive smoothing of empirical transfer function estimates
نویسندگان
چکیده
منابع مشابه
On Adaptive Smoothing of Empirical Transfer Function Estimates
The determination of the right resolution parameter when estimating frequency functions for linear systems is a trade-off between bias and variance. Traditional approaches, like “window-closing” employ a global resolution parameter – the window width – that is tuned by ad hoc methods, usually visual inspection of the results. Here we suggest an adaptive method that tunes such parameters by an a...
متن کاملDispersive Estimates, Strichartz Estimates and Smoothing Effects
In this short expository note, we will discuss three subjects: dispersive estimates, Strichartz estimates and smoothing effects which are of great importance in the study of dispersive equations. We will focus on the Euclidean setting and try to derive the interplay among above three objects. We will mainly focus on linear problems in various contexts. Some nonlinear application will also be me...
متن کاملNonparametric Renewal Function Estimation and Smoothing by Empirical Data
We consider an estimate of the renewal function (rf) using a limited number of independent observations of the interarrival times for an unknown interarrival-time distribution (itd). The nonparametric estimate is derived from the rf-representation as series of distribution functions (dfs) of consecutive arrival times using a finite summation and approximations of the latter by empirical dfs. Du...
متن کاملcompactifications and function spaces on weighted semigruops
chapter one is devoted to a moderate discussion on preliminaries, according to our requirements. chapter two which is based on our work in (24) is devoted introducting weighted semigroups (s, w), and studying some famous function spaces on them, especially the relations between go (s, w) and other function speces are invesigated. in fact this chapter is a complement to (32). one of the main fea...
15 صفحه اولFunction Estimation Using Data Adaptive Kernel Smoothers - How Much Smoothing?
We consider a common problem in physics: How to estimate a smooth function given noisy measurements. We assume that the unknown signal is measured at N different times, {ti: i = 1, . . . N} and that the measurements, {yi}, have been contaminated by additive noise. Thus the measurements satisfy yi = g(ti) + i, where g(t) is the unknown signal and i are random errors. For simplicity, we assume th...
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: Control Engineering Practice
سال: 2000
ISSN: 0967-0661
DOI: 10.1016/s0967-0661(00)00065-4