As one of the recently proposed algorithms for sparse system identification, l0 norm constraint Least Mean Square (l0-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of l0-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents compre...

A robust sparse least-mean mixture-norm (LMMN) algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path wireless channel. The proposed algorithm is implemented via integrating a correntropy-induced metric (CIM) penalty into the conventional LMMN algorithm to modify the basic cost function, which is denoted as the CIM-based LMMN (CIM-LMMN) algor...

The ADAptive LINear Element (ADALINE) neural network uses Least Mean Square (LMS) learning rule. This paper presents a comprehensive comparison of three different variable learning rate (VLR) parameter LMS algorithms, for the generalized ADALINE neural network paradigms. These algorithms are used to adjust the weights of the ADALINE neural network, which are tested under three different applica...

We have designed and simulated two techniques for acoustic echo cancellation. These systems are based upon a least-mean-square (LMS) adaptive algorithm and uses multi sub and sub band technique. A comparative study of both methods has been carried out.

The Least Mean Square algorithm has been used for estimation of auto regressive processes before [1]. Here in, a modified LMS algorithm is presented that can be used for estimation of processes with high correlation. Board level experiments are carried out to test the effectiveness of the algorithm in active noise cancellation. Results are summarized in the end.

Ahstpact-Convergence properties of a continuously adaptive digital lattice filter. used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curve...

In this paper, a Code-Jump (CJ) assisted Least Mean Square (LMS) calibration algorithm for high resolution successive approximation register (SAR) analog-to-digital converter (ADC) is proposed. This hybird does not require any specific input signal and has no impact on the ADC overall gain error. The revised LMS iteration used to improve accuracy of lowest bits (LSBs) capacitor in capacitive-di...

Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applic...

—The so-called constrained least mean-square algorithm is one of the most commonly used linear-equality-constrained adaptive filtering algorithms. Its main advantages are adaptability and relative simplicity. In order to gain theoretical insights into the performance of this algorithm, we examine its mean-square convergence and derive an expression for its steady-state mean-square deviation. Ou...