This paper describes the concept of adaptive linear combiner adds up the intermediate estimates at the output of each prediction stage to give a final estimate of the RLS–LMS predictor. In the RLS–LMS predictor, the first prediction stage is a simple first-order predictor with a fixed coefficient value1.The second prediction stage uses the recursive least square algorithm to adaptively update the predictor coefficients. The subsequent prediction stages use the normalized least mean square algorithm to update the predictor coefficients. The coefficients of the linear combiner are then updated using the sign–sign least mean square algorithm. In chapter1 an adaptive filter is defined as a self-designing system that relies for its operation on ...
It is shown that the commonly used adaptive algorithms are closely related to each other and can be ...
In this paper, the average of the steady state excess mean square error (ASEMSE) of the least mean k...
An alternative structure for adaptive linear prediction is proposed in which the adaptive filter is ...
Abstract: This paper presents a performance analysis of three categories of adaptive filtering algor...
The Recursive Least Squares algorithm (RLS) is utilized in digital signal processing as an adaptive ...
Recently we have proposed a recursive estimator for Reuyi's quadratic entropy. This estimator c...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
Lossless image coding is an essential requirement for medical imaging applications. Lossless image c...
This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) ada...
In this paper, linear prediction of signals is realized with an adaptive filter structure using a cl...
Abstract — In practical application, the statistical characteristics of signal and noise are usually...
In this paper, we develop adaptive linear prediction filters in the framework of maximum a posterior...
The paper presents a new family of RLS adaptive filter-ing algorithms developed for non-stationary s...
Filtering algorithm uses a variable step-size and the first order recursive estimation of the correl...
In this paper we consider a recursive least squares (RLS) adaptive filtering problem where the input...
It is shown that the commonly used adaptive algorithms are closely related to each other and can be ...
In this paper, the average of the steady state excess mean square error (ASEMSE) of the least mean k...
An alternative structure for adaptive linear prediction is proposed in which the adaptive filter is ...
Abstract: This paper presents a performance analysis of three categories of adaptive filtering algor...
The Recursive Least Squares algorithm (RLS) is utilized in digital signal processing as an adaptive ...
Recently we have proposed a recursive estimator for Reuyi's quadratic entropy. This estimator c...
In this book, the authors provide insights into the basics of adaptive filtering, which are particul...
Lossless image coding is an essential requirement for medical imaging applications. Lossless image c...
This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) ada...
In this paper, linear prediction of signals is realized with an adaptive filter structure using a cl...
Abstract — In practical application, the statistical characteristics of signal and noise are usually...
In this paper, we develop adaptive linear prediction filters in the framework of maximum a posterior...
The paper presents a new family of RLS adaptive filter-ing algorithms developed for non-stationary s...
Filtering algorithm uses a variable step-size and the first order recursive estimation of the correl...
In this paper we consider a recursive least squares (RLS) adaptive filtering problem where the input...
It is shown that the commonly used adaptive algorithms are closely related to each other and can be ...
In this paper, the average of the steady state excess mean square error (ASEMSE) of the least mean k...
An alternative structure for adaptive linear prediction is proposed in which the adaptive filter is ...