This paper studies a class of algorithms called Natural Gradient (NG) algorithms, and their approximations, known as ANG algorithms. The LMS algorithm is derived within the NG framework, and a family of LMS variants that exploit sparsity is derived. Mean squared error (MSE) analysis of the family of ANG algorithms is provided, and it is shown that if the system is sparse, then the new algorithm...

Although the normalized least mean square (NLMS) algorithm is robust, it suffers from low convergence speed if driven by highly correlated input signals. One method presented to overcome this problem is the Ozeki/Umeda affine projection (AP) algorithm. The algorithm applies update directions that are orthogonal to the last P input vectors and thus allows decorrelation of an AR(P) input process,...

This paper proposes an adaptive algorithm, which can be pipelined, as an extension of the delayed least mean square (DLMS) adaptive algorithm. The proposed algorithm provides a capability to achieve high throughput with less degradation of the convergence characteristic than the DLMS algorithm. An architecture for pipelined implementation of the proposed algorithm is considered, and based on th...

A combination of two complex normalized least mean square (NLMS) adaptive filters that adapt on the same input signal at the same time is investigated. One of the filters has a large and the other one has a small step size. The outputs of the filters are combined together through a mixing parameter λ . This combination is an interesting new way of achieving simultaneously a fast initial converg...

A new adaptive step size adjustment least mean square (LMS) algorithm is presented in this paper. The proposed algorithm modified the existing LMS using the estimated output error as an important component for the modification of the step size. Experiment results demonstrate that application of the new algorithm leads to a significant gain in SNR (signal-to-noise ratio), thus visibly reduces th...

The effects of DC offsets on four variations of the stochastic gradient algorithm are analyzed to determine the most appropriate algorithm for hardware implementation. The output mean squared error (MSE) performance in the presence of DC offsets is evaluated and compared with computer simulations for each of the algorithms assuming a Gaussian input distribution.

In this correspondence, a least mean squares (LMS)-based algorithm is devised for unbiased system identification in the presence of white input and output noise, assuming that the ratio of the noise powers is known. The proposed approach aims to minimize the mean square value of the equation-error function under a constant-norm constraint and is equivalent to minimizing a modified mean square e...

We study the effect of fading in the communication channels between nodes on the performance of the incremental least mean square (ILMS) algorithm. We derive steadystate performance metrics, including the mean-square deviation (MSD), excess mean-square error (EMSE), and mean-square error (MSE). We obtain the sufficient conditions to ensure meansquare convergence, and verify our results through ...

A new LMS-type adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady state behavior of the algorithm are analyzed. These results reduce to well-known ones when specialized to...

In this paper, an estimation of the Gaussian noise variance based on observed (measured) maximums of subsets of samples is given. Circumstances of the measurement environment being limited, only maximums of subsets of samples are available and the non-constant variance of the Gaussian noise can be estimated. In the case of power line noise, the variance of the zero mean Gaussian noise is a peri...