Least-mean Mixed-norm Research Articles

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.

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.

Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

Adaptive filtering algorithms based on higher-order statistics are proposed for multi-dimensional signal processing in geometric algebra (GA) space. In this paper, the proposed adaptive filtering algorithms utilize the advantage of GA theory in multi-dimensional signal processing to represent a multi-dimensional signal as a GA multivector. In addition, the original least-mean fourth (LMF) and least-mean mixed-norm (LMMN) adaptive filtering algorithms are extended to GA space for multi-dimensional signal processing. Both the proposed GA-based least-mean fourth (GA-LMF) and GA-based least-mean mixed-norm (GA-LMMN) algorithms need to minimize cost functions based on higher-order statistics of the error signal in GA space. The simulation results show that the proposed GA-LMF algorithm performs better in terms of convergence rate and steady-state error under a much smaller step size. The proposed GA-LMMN algorithm makes up for the instability of GA-LMF as the step size increases, and its performance is more stable in mean absolute error and convergence rate.

LMMN-Based Adaptive Control for Power Quality Improvement of Grid Intertie Wind–PV System

A new topology comprising of wind-turbine-driven synchronous generator (SG) and solar photovoltaic (PV) array for renewable energy harvesting is proposed in this paper. The stochastic inputs for the proposed system are agitated using nonlinear time-dependent parameters such as varying wind speed and changing solar insolation. The speed variations are absorbed using back-to-back interfaced power electronic converters (PECs), namely, SG side converter (SGC) and utility grid side converter (UGC) with a common dc link where a solar PV array is tied directly. The power injection into the utility grid is leveled by the optimal utilization of the PECs. The SGC uses vector control (VC) for speed control of the SG and maintains unity power factor (UPF) at stator terminals. UGC acquires its switching pulses with proper application of least mean mixed-norm (LMMN) control technique. The new application of LMMN control scheme is used for the compensation of harmonics and for the extraction of fundamental load component. The dc-link voltage is regulated using a proportional integral controller. A prototype is developed and tested under different conditions of sudden changes in load, wind-velocity variations, and varying solar PV insolation. The power sharing scheme proves to be effective. The power quality issues are also addressed and mitigated effectively. The performance is exhibited for the validation of the proposed system and its control.

Control of Single-Phase Solar Power Generation System With Universal Active Power Filter Capabilities Using Least Mean Mixed-Norm (LMMN)-Based Adaptive Filtering Method

This paper deals with the control of a single-phase grid-tied solar photovoltaic (SPV) power generation system with an universal active power filter (UAPF) capabilities. The SPV-UAPF system consists of series and shunt voltage-source inverters (VSIs). The shunt VSI exports the real power extracted from the photovoltaic panels to the grid and local loads. In addition to handling the real power, the shunt VSI provides compensation of reactive and harmonic currents generated by the loads. The reference signals required for the control of the shunt and series VSIs of the SPV-UAPF system are estimated using the least mean mixed-norm (LMMN) adaptive identification algorithm. The performance of the LMMN-based control of the SPV-UAPF system with series shunt compensation capabilities is demonstrated using MATLAB/Simulink-based computer simulations and hardware-in-the-loop-based experimental results under various operating conditions such as solar irradiance variation, voltage sag, swell, and current harmonics.

Sparse least mean mixed‐norm adaptive filtering algorithms for sparse channel estimation applications

SummarySparse least‐mean mixed‐norm (LMMN) algorithms are developed to improve the estimation performance for sparse channel estimation applications. Both the benefits of the least mean fourth and least mean square algorithms are utilized to exploit a type of sparse LMMN algorithms. The proposed sparse‐aware LMMN algorithms are implemented by integrating anl1‐norm or log‐sum function into the cost function of traditional LMMN algorithm so that they can exploit the sparse properties of the broadband multi‐path channel and achieve better channel estimation performance. The proposed sparse LMMN algorithms are equal to adding an amazing zero‐attractor in the update equation of the traditional LMMN algorithm, which aim to speed up the convergence. The channel estimation performance of the proposed sparse LMMN algorithms are evaluated over a sparse broadband multi‐path channel to verify their effectiveness. Simulation results depict that the sparse LMMN algorithms are superior to the previously reported sparse‐aware least mean square/fourth, least mean fourth and least mean square and their corresponding sparse‐aware algorithms in terms of both the convergence and steady‐state behavior when the broadband multi‐path channel is sparse. Copyright © 2016 John Wiley & Sons, Ltd.

Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm

Convergence and steady-state analyses of a least-mean mixed-norm adaptive algorithm are presented. This is formed as a convex mixture of the mean-square and the mean-fourth cost functions. The local exponential stability of the algorithm is shown by application of the deterministic averaging analysis and the total stability theorem. A theoretical misadjustment expression is then obtained by using the ordinary-differential-equation method. Simulation studies are presented to support the theoretical findings. The results demonstrate the advantage of mixing error norms in adaptive filtering when the measurement noise is composed of a linear combination of long-tail and short-tail noise distributions.