A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms
نویسندگان
چکیده
منابع مشابه
Splitting the recursive least-squares algorithm
Exponentially weighted recursive least-squares (RLS) algorithms are commonly used for fast adaptation. In many cases the input signals are continuous-time. Either a fully analog implementation of the RLS algorithm is applied or the input data are sampled by analog-to-digital (AD) converters to be processed digitally. Although a digital realization is usually the preferred choice, it becomes unf...
متن کاملMultiple Concurrent Recursive Least Squares Identification
A new algorithm, multiple concurrent recursive least squares (MCRLS) is developed for parameter estimation in a system having a set of governing equations describing its behavior that cannot be manipulated into a form allowing (direct) linear regression of the unknown parameters. In this algorithm, the single nonlinear problem is segmented into two or more separate linear problems, thereby enab...
متن کاملA recursive total least squares algorithm for deconvolution problems
Deconvolution problems are encountered in signal processing applications where an unknown input signal can only be observed after propagation through one or more noise corrupted FIR channels. The first step in recovering the input usually entails an estimation of the FIR channels through training based or blind algorithms. The ’standard’ procedure then uses least squares estimation to recover t...
متن کاملA recursive algorithm for nonlinear least-squares problems
The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). The error of the MEKF algorithm is proved to be exponentially bounded. Batch and iterated versions of th...
متن کاملH " Bounds for the Recursive - Least - Squares Algorithm *
We obtain upper and lower bounds for the H" norm of the RLS (Recursive-Least-Squares) algorithm. The H" norm may be regarded aa the worst-case energy gain from the disturbances to the prediction errors, and is therefore a measure of the robustness of an algorithm to perturbations and model uncertainty. Our results allow one to compare the robustness of RLS compared to the LMS (Least-Mean-Square...
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: Algorithms
سال: 2020
ISSN: 1999-4893
DOI: 10.3390/a13060135