This paper presents a unifying view of various error nonlinearities that are used in least mean square (LMS) adaptation such as the least mean fourth (LMF) algorithm and its family and the least-mean mixed-norm algorithm. Speci cally, it is shown that the LMS algorithm and its errormodi ed variants are approximations of two recently developed optimum nonlinearities which are expressed in terms ...

For the ANSYS CFD codes CFX and Fluent coupling links to several 1D codes exist allowing for a wide range of multi-physics applications. As an example, a flexible coupling infrastructure in terms of supported multiphysics coupling conditions between ANSYS CFX and LMS AMESim is outlined. Examples of the wide range of coupling solutions of ANSYS CFD (CFX and Fluent) and coupled 1D-3D applications...

In this brief, we develop an least mean square (LMS) algorithm that converge in a statistical sense to the global minimum of the mean square error (MSE) objective function. This is accomplished by estimating the gradient as a smoothed version of the MSE. The smoothed MSE objective begins as a convex functional in the mean. The amount of dispersion or smoothing is reduced, such that over time it...

Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defi...

This paper presents a Comparative Study of NLMS (Normalized Least Mean Square) and ENSS (Error Normalized Step Size) LMS (Least Mean Square) algorithms. For this System Identification (An Adaptive Filter Application) is considered. Three performances Criterion are utilized in this study: Minimum Mean Square error (MSE), Convergence Speed, the Algorithm Execution Time. The Step Size Parameter (μ...

A simple model characterizing the convergence properties of an adaptive digital lattice filter using gradient algorithms has been reported [ 11. This model is extended to the least mean square (LMS) lattice joint process estimator [SI, and to the least squares (LS) lattice and “fast” Kalman algorithms [9] -[16]. The models in each case are compared with computer simulation. The single-stage LMS...

The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subf...

A fundamental relationship exists between the quality of an adaptive solution and the amount of data used in obtaining it. Quality is defined here in terms of “misadjustment,” the ratio of the excess mean square error (mse) in an adaptive solution to the min imum possible mse. The higher the misadjustment, the lower the quality is. The quality of the exact least squares solution is compared wit...

Least mean square (LMS)-based adaptive algorithms have attracted much attention due to their low computational complexity and reliable recovery capability. To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods have been proposed based on different sparse penalties, such as l1-norm LMS or zeroattracting LMS (ZA-LMS), reweighted ZA-LMS, and lp-norm LMS. However, th...