[4]. In this paper, we propose a more general structure of that of [4], the generalized square-error-regularized LMS (GSER-LMS) algorithm. Extensive simulation results demonstrate that our GSER-LMS outperforms existing schemes in speed of convergence, tracking ability, and low mis-adjustment.

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The Generalized Square-Error-Regularized LMS Algorithm

This paper proposes new two adaptive beamforming algorithms based on a combined method. The first proposal combination method includes merging pure Conjugate Gradient Method (CGM) with pure Normalized Least Mean Square (NLMS) algorithms, so that the first a new algorithm is called as CGM-NLMS. While the second proposed algorithm merges pure CGM with a Modified NLMS algorithm (MNLMS). The MNLMS algorithm is regarded as a variable regularization parameter that is fixed in the conventional NLMS algorithm. Then the new combination algorithm is called as CGM-MNLMS algorithm. Through simulation results of adaptive beamforming system using an Additive White Gaussian Noise (AWGN) channel model, the two new proposed algorithms provides fast convergence time, higher interference suppression capability and low level of MSD, and MSE at steady state compared with the pure CGM and pure NLMS algorithms.

Performance enhancement of adaptive beamforming algorithms based on a combination method

The purpose of a variable step-size normalized LMS (VSS-NLMS) filter is to solve the problem of fast convergence or low mis-adjustment. While most VSS-NLMS algorithms need to tune several parameters for better performance, we introduced a tuning-free square-error-based VSS-NLMS algorithm recently . In this paper, we propose a more general structure of that of the generalized square-error-regularized LMS (GSER-LMS) algorithm. Extensive simulation results demonstrate that our GSER-LMS algorithm outperforms several existing schemes in speed of convergence, tracking ability, and low mis-adjustment.

A New Square-Error-Regularized NLMS

Solar photovoltaic (SPV) power generating systems requires a robust and reliable control to perform the multitasking operation under different operating conditions. This paper presents an application of new square-error regularized NLMS (normalized least mean square) algorithm for integrating SPV system to the utility grid. Here, variable step-size regularization is adopted in order to fix the problem associated with fixed regularization including high steady state error and low convergence speed. This adaptive time varying parameter is designed by taking the inverse of weighted square error and implemented to the fundamental weight extraction mechanism for generating the inverter reference current. Subsequently, the proposed control algorithm enables this inverter to perform the multiple objectives including grid current harmonic alleviation, reactive current support, power factor correction and load balancing without affecting their major task of real power injection. Moreover, this control ably regulates the DC-bus voltage within the acceptable limits to maintain good dynamic response under transient conditions. The adopted two-stage grid connected SPV system with proposed control has been modelled using MATLAB/Simulink software for performing simulation study. Further these simulation results are verified through laboratory developed prototype using waijung environment of ARM cortex M4 microcontroller.

Regularized NLMS Control for Power Quality Improvement in Grid-connected PV Systems

This paper proposes a new combined algorithm for performance enhancement of smart antenna systems in mobile communications. The new combined algorithm (NCA) uses sample matrix inversion (SMI) initialization with a modified version of normalized least mean square (NLMS) algorithm (MNLMS). The MNLMS algorithm is regarded as time-varying regularization parameter ϵ(k) that is fixed in the conventional NLMS algorithm. The MNLMS algorithm uses a reciprocal of the estimation error square term in the denominator of the learning rate of NLMS instead of fixed regularization parameter (ϵ). Through simulation results of smart antennas for an additive white Gaussian noise channel model, the NCA algorithm achieves about 15 and 19 dB improvement in interference suppression compared with NLMS and another proposed previously matrix inversion NLMS (MI-NLMS) algorithms, respectively. While the NLMS algorithm starts to converge from 50 iterations, the MI-NLMS and NCA algorithms start to converge from the initial iter...

New Combined Adaptive (NCA) Algorithm for Smart Antenna System Applications

The purpose of a variable step-size normalized LMS filter is to solve the dilemma of fast convergence or low steady-state error associated with the fixed regularized NLMS. By employing the inverse of weighted square-error as the time-varying regularization parameter, we introduce a new regularization for NLMS algorithms. Extensive simulation results demonstrate that our proposed algorithm outperforms existing schemes in speed of convergence, tracking ability, and low misadjustment.

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Yung Ning Yang

Papers

A Square-Error-Based Regularization for Normalized LMS Algorithms

Prescribed performance-based robust inverse optimal control for spacecraft proximity operations with safety concern