NONLINEAR STRUCTURE IDENTIFICATION WITH LINEAR LEAST SQUARES AND ANOVA
نویسندگان
چکیده
منابع مشابه
Linear Least Squares Estimates and Nonlinear Means
The consistency and asymptotic normality of a linear least squares estimate of the form (X,X)-X’Y when the mean is not X/I is investigated in this paper. The least squares estimate is a consistent estimate of the best linear approximation of the true mean function for the design chosen. The asymptotic normality of the least squares estimate depends on the design and the asymptotic mean may not ...
متن کاملLinear and Non-linear System Identification Using Separable Least-Squares
We demonstrate how the separable least-squares technique of Golub and Pereyra can be exploited in the identiication of both linear and non-linear systems based on the prediction error formulation. The model classes to be considered here are the output error model and innovations model in the linear case and the Wiener system in the non-linear case. This technique together with a suitable choice...
متن کاملIdentification of Linear Systems with Errors in Variables Using Separable Nonlinear Least-squares
It is well-known that the least-squares identification method generally gives biased parameter estimates when the observed input-output data are corrupted with noise. If the noise acting on both the input and output is white, and if the noise variances are known, or if estimates of the noise variances are available, then the principle of biasedcompensated least-squares (CLS) can readily be used...
متن کاملNonlinear Least-squares Estimation
The paper uses empirical process techniques to study the asymptotics of the least-squares estimator for the fitting of a nonlinear regression function. By combining and extending ideas of Wu and Van de Geer, it establishes new consistency and central limit theorems that hold under only second moment assumptions on the errors. An application to a delicate example of Wu’s illustrates the use of t...
متن کاملNonlinear least squares and regularization
I present and discuss some general ideas about iterative nonlinear output least-squares methods. The main result is that, if it is possible to do forward modeling on a physical problem in a way that permits the output (i.e., the predicted values of some physical parameter that could be measured) and the rst derivative of the same output with respect to the model parameters (whatever they may be...
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: IFAC Proceedings Volumes
سال: 2005
ISSN: 1474-6670
DOI: 10.3182/20050703-6-cz-1902.00009