Maximum likelihood identification of noisy input-output models
نویسندگان
چکیده
This paper deals with the identification of errors–in–variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise–free input is an arbitrary signal, not necessarily periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed. As some other EIV estimators, this method assumes that the ratio of the noise variances is known.
منابع مشابه
Noise Effects on Modal Parameters Extraction of Horizontal Tailplane by Singular Value Decomposition Method Based on Output Only Modal Analysis
According to the great importance of safety in aerospace industries, identification of dynamic parameters of related equipment by experimental tests in operating conditions has been in focus. Due to the existence of noise sources in these conditions the probability of fault occurrence may increases. This study investigates the effects of noise in the process of modal parameters identification b...
متن کاملRecursive Identification for Dynamic Linear Systems from Noisy Input-Output Measurements
Errors-in-variables (EIV) model is a kind of model with not only noisy output but also noisy input measurements, which can be used for systemmodeling in many engineering applications. However, the identification for EIVmodel is much complicated due to the input noises. This paper focuses on the adaptive identification problem of real-time EIV models. Some derivation errors in an accuracy resear...
متن کاملParameter Estimation of Systems Described by the Relation with Noisy Observations
In this paper the problem of parameter estimation of an input – output system is discussed. It is assumed that the system is described by the relation known with accuracy to some parameters. The possible noisy observations of system are described. The estimation algorithm based on maximum likelihood method is proposed. The presented approach is illustrated by analytical examples.
متن کاملUnbiased equation-error based algorithms for efficient system identification using noisy measurements
Based on the equation-error approach, two constrained weighted least squares algorithms are developed for unbiased infinite impulse response system identification. Both white input and output noise are present, and the ratio of the noise powers is known. Through a weighting matrix, the first algorithm uses a generalized unit-norm constraint which is a generalization of the Koopmans–Levin method...
متن کاملSecond-Order Volterra System Identification With Noisy Input-Output Measurements
System identification with noisy input–output measurements has been dominantly addressed through the optimization of the mean-squared-error criterion (MSE), especially in adaptive filtering. MSE is known to provide models that approximate the conditional expectation of the target output given the input; however, when the input signal is also contaminated by noise—a frequent occurrence—MSE yield...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Automatica
دوره 43 شماره
صفحات -
تاریخ انتشار 2007