Mean Mixed Norm Research Articles

A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications.

Recursive least squares (RLS)-based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.

Design and Analysis of the Shunt Active Power Filter with the ε-NSRLMMN Adaptive Algorithm for Power Quality Improvement in the Distribution System

In this article, an adaptive normalized Sign Regression Least Mean Mixed Norm (ε-NSRLMMN) algorithm-based control technique is developed for the Shunt Active Power Filter (SAPF) to compensate for the grid current harmonics and reactive power under balanced and unbalanced loading conditions. The adaptive ε-NSRLMMN control algorithm improves power quality and makes the source current sinusoidal and power factor (PF) close to unity. It is used for load compensation in a three-phase distribution network. The fundamental active component is extracted from the distorted load current using an adaptive technique. This control algorithm has the advantages of improved convergence, better stability, lower harmonic distortion, less complexity and less steady-state error than the Sign regressor least mean mixed norm (SRLMMN), Least mean square (LMS) and least mean fourth (LMF). Adaptive ε-NSRLMMN is used for learning the fundamental active weight components of the load current. The parameters of the proposed algorithm are trained in real-time. A traditional PI controller has been used to regulate the dc-link voltage of the Voltage Source Converter (VSC). The performance of the proposed algorithm has been justified by simulation and experimental results.

Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers

Although cascaded multistage adaptive noise cancellers have been employed before by researchers for multiple artifact removal from the ElectroCardioGram (ECG) signal, they all used the same adaptive algorithm in all the cascaded multi-stages for adjusting the adaptive filter weights. In this paper, we propose a cascaded 4-stage adaptive noise canceller for the removal of four artifacts present in the ECG signal, viz. baseline wander, motion artifacts, muscle artifacts, and 60 Hz Power Line Interference (PLI). We have investigated the performance of eight adaptive algorithms, viz. Least Mean Square (LMS), Least Mean Fourth (LMF), Least Mean Mixed-Norm (LMMN), Sign Regressor Least Mean Square (SRLMS), Sign Error Least Mean Square (SELMS), Sign-Sign Least Mean Square (SSLMS), Sign Regressor Least Mean Fourth (SRLMF), and Sign Regressor Least Mean Mixed-Norm (SRLMMN) in terms of Signal-to-Noise Ratio (SNR) improvement for removing the aforementioned four artifacts from the ECG signal. We employed the LMMN, LMF, LMMN, LMF algorithms in the proposed cascaded 4-stage adaptive noise canceller to remove the respective ECG artifacts as mentioned above. We succeeded in achieving an SNR improvement of 12.7319 dBs. The proposed cascaded 4-stage adaptive noise canceller employing the LMMN, LMF, LMMN, LMF algorithms outperforms those that employ the same algorithm in the four stages. One unique and powerful feature of our proposed cascaded 4-stage adaptive noise canceller is that it employs only those adaptive algorithms in the four stages, which are shown to be effective in removing the respective ECG artifacts as mentioned above. Such a scheme has not been investigated before in the literature.

Water Cycle Algorithm Based Parametric Tuning of Non-Negative LMMN Control of Grid Tied Renewable Energy Systems

ABSTRACT This work proposes a hydrological cycle-based optimisation technique for performance enhancement of a non-negative least mean mixed norm (NNLMMN) based voltage source control (VSC) for a three-phase grid integrated wind/photovoltaic (PV) system. Water cycle algorithm (WCA) is employed to tune the proportional–integral (PI) controller parameters of VSC to regulate voltage variations at DC link generated during dynamic load and wind conditions. This results in generation of a specific loss component (Iloss) of current to boost the performance of VSC. The NNLMMN control deals with system identification troubles with a non-negativity limitation in varied Gaussian noise conditions. It generates accurate Iloss to upgrade the ability of VSC to accurately extricate weight signals and the fundamental load current component in terms of improvement in DC link voltage (vdc) and total harmonic distortion. The proposed algorithm mitigates problems like voltage fluctuations besides performing operations like reactive power compensation, enhancement of power quality, load and power balancing at the point of coupling during steady-state and dynamic conditions. In conjunction with the NNLMMN algorithm, the proposed technique reduces the surplus mean square error iteratively, resulting in a compact expression of step size. The proposed topology stands out with an improved performance of vdc under wind gusts and the well-known bell-shaped irradiance curve. For relative evaluations, the performance of non-optimised PI and particle swarm optimised PI has been added to analyse the improved response of vdc and Iloss. Extensive simulation results obtained in MATLAB/Simulink are exhibited to corroborate the efficacy of the suggested system.

희소 시스템을 위한 희소 인식 인접 투사 Least Mean Mixed-norm 알고리즘

본 논문에서는 적응형 필터에서 사용되는 희소 시스템 식별을 위한 희소 식별 인접 투사 least mean mixed-norm (SAP-LMMN) 알고리즘을 제안한다. 제안하는 알고리즘은 희소성 페널티를 갖는 l₂ norm과 l₄ norm의 혼합 norm의 비용 함수로부터 유도된다. 우리는 희소 페널티로 lSUB0/SUB norm 또는 l₁ norm을 사용하였고, 두 norm으로부터 유도된 zero attracting 항들은 적응형 필터 계수의 업데이트 과정에서 계수의 희소성을 유발하여 수렴을 가속한다. 제안하는 알고리즘의 성능을 검증하기 위해 희소 시스템 식별 시나리오에서 시뮬레이션을 진행한다. 음향 반향 제거 시스템 시뮬레이션을 통해 제안한 알고리즘이 기존의 알고리즘들보다 우수한 성능을 가진다는 것을 보인다.

An Analytical Model for Predicting the Convergence Behavior of the Least Mean Mixed-Norm (LMMN) Algorithm

An Analytical Model for Predicting the Convergence Behavior of the Least Mean Mixed-Norm (LMMN) Algorithm

Grid tied solar PV system with power quality enhancement using adaptive generalized maximum Versoria criterion

A three phase grid tied solar photovoltaic (PV) system with power quality compensation features is presented in this paper. This system is operated to transfer power generated from solar PV array to feed linear and nonlinear loads along with compensation of several power quality (PQ) issues such as harmonics, redundant reactive power and load unbalancing. The generated DC power from PV array is converted into AC by implementing a three phase voltage source converter (VSC). In order to transfer active power and mitigate PQ problems, an efficient control technique is required for grid tied solar PV system. This work presents the use of adaptive generalized maximum Versoria criterion (AGMVC) controller for VSC used in solar PV energy conversion system. Efficient utilization of solar PV array is accomplished by using Perturb and Observe based maximum power point tracking (MPPT) algorithm. The experimental setup of grid integrated PV system is implemented in the laboratory using IGBT based VSC and DSP (dSPACE DS-1202). Performance of AGMVC control technique is verified experimentally using laboratory prototype. This control technique is compared with different conventional controllers such as synchronous reference frame theory (SRFT) and instantaneous reactive power theory (IRPT) along with recently developed weight based controllers viz. least mean square (LMS), least mean mixed norm (LMMN) and normalized kernel least mean fourth-neural network (NKLMF-NN). Numerous terms such as fundamental weight convergence, steady state error, computational complexity, phase lock loop (PLL) requirement and harmonic compensation capability are considered for comparison purpose of AGMVC with the above listed control techniques. The system performance is verified according to IEEE-519 standard.

Real‐time implementation of active shunt compensator with adaptive SRLMMN control technique for power quality improvement in the distribution system

In this work, the development of sign regressor least mean mixed norm (SRLMMN) control technique for a distribution static compensator (D-STATCOM) is presented. This control technique extracts fundamental weight components from the non-sinusoidal load currents and generates reference grid currents. D-STATCOM is developed for harmonics eradication, reactive power injection and load balancing and its performance is investigated in several operating modes. The performance of SRLMMN control is compared with recursive least square (RLS) and variable step least mean square (VSLMS) control techniques in terms of convergence, steady-state error, harmonics elimination, sample time and computation complexity. The major advantages of SRLMMN control technique, are fast convergence, less steady-state error, low total harmonic distortion (THD) and offers less computation complexity when compared with RLS and VSLMS. A laboratory-scale prototype of compensator is realised using a voltage source converter with the controller implemented in the dSPACE-MicroLabBox. Both MATLAB simulation and experimental results are included to demonstrate the performance of shunt compensator under steady-state and dynamic loadings. The developed SRLMMN control technique mitigates power quality problems and effectively suppresses THD observed in the grid current with reference to IEEE Standard 519-2014.

A Family of Blocked Proportionate Hybrid Error Criterion Algorithms

A family of blocked proportionate normalized least mean mixed-norm (BPNLMMN) algorithms using hybrid error criterion are devised and investigated for estimating block-sparse channels that are always considered in networks and satellite echo channels. The derived BPNLMMN algorithms are realized by considering two different errors in the least mean algorithm and incorporating hybrid-norm constraints into the constructed cost function to fully use the prior cluster-sparse information in the systems to be estimated. We carefully derive the BPNLMMN algorithms and provide a completed analysis of the BPNLMMN algorithms for cluster channel estimations. The obtained results from various simulations illustrate that the BPNLMMN algorithms provide superior performance under various inputs and perform better than existing least mean mixed-norm algorithms.

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

We propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e., the transient and the steady-state analyses, for the proportionate update based algorithms, e.g., the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios.

Least mean mixed-norm adaptive filtering

A new family of stochastic gradient adaptive filter algorithms is proposed which is based on mixed error norms. These algorithms combine the advantages of different error norms, for example the conventional, relatively well-behaved, least mean square algorithm and the more sensitive, but better converging, least mean fourth algorithm. A mixing parameter is included which controls the proportions of the error norms and offers an extra degree of freedom within the adaptation. A system identification simulation is used to demonstrate the performance of a least mean mixed-norm (square and fourth) algorithm.