Robust Regularized Recursive Least M-estimate Algorithm for Sparse System Identification
نویسندگان
چکیده
منابع مشابه
Adaptive sparse system identification using normalized least mean fourth algorithm
Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high-identification performan...
متن کاملA Sparse Regularized Least-Squares Preference Learning Algorithm
Learning preferences between objects constitutes a challenging task that notably differs from standard classification or regression problems. The objective involves prediction of ordering of the data points. Furthermore, methods for learning preference relations usually are computationally more demanding than standard classification or regression methods. Recently, we have proposed a kernel bas...
متن کاملA robust, parallelizable, O(m), a posteriori recursive least squares algorithm for efficient adaptive filtering
This correspondence presents a new recursive least squares (RLS) adaptive algorithm. The proposed computational scheme uses a finite window by means of a lemma for the system matrix inversion that is, for the first time, stated and proven here. The new algorithm has excellent tracking capabilities. Moreover, its particular structure allows for stabilization by means of a quite simple method. It...
متن کاملUnsupervised robust recursive least-squares algorithm for impulsive noise filtering
A robust recursive least-squares (RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired signal. By minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function, a possible impulse-corrupted signal is prevented from entering the filter’s weight updating scheme. Moreover, a multi-step adaptive...
متن کاملDynamically Regularized Fast Recursive Least Squares
This paper introduces a dynamically regularized fast recursive least squares (DR-FRLS) adaptive filtering algorithm. Numerically stabilized FRLS algorithms exhibit reliable and fast convergence with low complexity even when the excitation signal is highly self-correlated. FRLS still suffers from instability, however, when the condition number of the implicit excitation sample covariance matrix ...
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-PapersOnLine
سال: 2019
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2019.12.424