Adaptive Fourier linear combiner based on modified least mean Kurtosis algorithm for the processing of sinusoidal signals

2019 ◽  
pp. 014233121988917
Author(s):  
Engin Cemal Mengüç
Keyword(s):  
Cost Function ◽  
Design Procedure ◽  
Sinusoidal Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Step Size ◽  
Hand Tremor ◽  
The Mean ◽  
Sinusoidal Signals ◽  
Weight Coefficients

This study introduces an adaptive Fourier linear combiner (FLC) based on a modified least mean kurtosis (LMK) algorithm in order to effectively process sinusoidal signals, which we call FLC-LMK algorithm. In the design procedure of the proposed FLC-LMK algorithm, the classical kurtosis-based cost function is first modified for only sinusoidal signal distributions instead of Gaussian. Then, the FLC-LMK algorithm is derived from the minimization of this cost function and thus updates the weight coefficients of the FLC structure so as to directly process sinusoidal signals. Moreover, in this study, the convergence in the mean of the proposed FLC-LMK algorithm is analysed in order to determine the lower and upper bounds of its step size parameter. The most important contributions of the use of the proposed algorithm in the FLC structure are that it increases the convergence rate, decreases the steady-state error level and also has a robust behaviour against sinusoidal signal distributions due to its modified cost function. The performance of the proposed FLC-LMK algorithm is evaluated on the synthetic and real-world pathological hand tremor data by comparing with that of the FLC based on the classical least mean square (LMS) (FLC-LMS) algorithm. The simulation results support the mentioned properties of the proposed FLC-LMK algorithm.

scholarly journals Stability Conditions for the Leaky LMS Algorithm Based on Control Theory Analysis

2016 ◽  
Vol 41 (4) ◽  
pp. 731-739 ◽  
Author(s):  
Dariusz Bismor ◽  
Marek Pawelczyk
Keyword(s):  
Point Of View ◽  
Lms Algorithm ◽  
Theoretic Approach ◽  
Mean Square ◽  
Least Mean Square ◽  
Theory Analysis ◽  
Step Size ◽  
Practical Applications ◽  
The Mean ◽  
Points Of View

AbstractThe Least Mean Square (LMS) algorithm and its variants are currently the most frequently used adaptation algorithms; therefore, it is desirable to understand them thoroughly from both theoretical and practical points of view. One of the main aspects studied in the literature is the influence of the step size on stability or convergence of LMS-based algorithms. Different publications provide different stability upper bounds, but a lower bound is always set to zero. However, they are mostly based on statistical analysis. In this paper we show, by means of control theoretic analysis confirmed by simulations, that for the leaky LMS algorithm, a small negative step size is allowed. Moreover, the control theoretic approach alows to minimize the number of assumptions necessary to prove the new condition. Thus, although a positive step size is fully justified for practical applications since it reduces the mean-square error, knowledge about an allowed small negative step size is important from a cognitive point of view.

Application of Improved LMS Adaptive Algorithm in Acoustics Pyrometry

2014 ◽  
Vol 672-674 ◽  
pp. 2025-2028
Author(s):  
Shi Ping Zhang ◽  
Guo Qing Shen ◽  
Lian Suo An
Keyword(s):  
Temperature Measurement ◽  
Discrete Cosine Transform ◽  
Time Delay Estimation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Boiler Furnace ◽  
Cosine Transform ◽  
Variable Step

Acoustic pyrometry is a comparatively advanced method of temperature measurement developed in recent years, which possesses the essential characteristics of traditional temperature measurement approach. Considering the interferences, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above. In order to make the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses the sliding forgetting-weighted window. The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

scholarly journals New feedforward filtered-x least mean square algorithm with variable step size for active vibration control

2018 ◽  
Vol 38 (1) ◽  
pp. 187-198 ◽  
Author(s):  
Yubin Fang ◽  
Xiaojin Zhu ◽  
Zhiyuan Gao ◽  
Jiaming Hu ◽  
Jian Wu
Keyword(s):  
Vibration Control ◽  
Active Vibration Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Large Step ◽  
Variable Step ◽  
Active Vibration

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

scholarly journals Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System

2018 ◽  
Vol 7 (2.17) ◽  
pp. 74 ◽  
Author(s):  
Asiya Sulthana ◽  
Md Zia Ur Rahman
Keyword(s):  
Health Care ◽  
Monitoring System ◽  
Mean Squared Error ◽  
Lms Algorithm ◽  
Frequency Resolution ◽  
Variable Step Size ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Health Care Monitoring

An increasing number of elderly­­­­ and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.  

A Variable Step Size LMS Based on Sparsity for System Identification

2013 ◽  
Vol 475-476 ◽  
pp. 1060-1066
Author(s):  
X.Q. Chen ◽  
Hua Ju ◽  
Wei Fan ◽  
W.G. Huang ◽  
Z.K. Zhu
Keyword(s):  
Steady State ◽  
System Identification ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Practical Applications ◽  
Variable Step ◽  
Size Adjustment ◽  
State Error

In many practical applications, the impulse responses of the unknown system are sparse. However, the standard Least Mean Square (LMS) algorithm does not make full use of the sparsity, and the general sparse LMS algorithms increase steady-state error because of giving much large attraction to the small factor. In order to improve the performance of sparse system identification, we propose a new algorithm which introduces a variable step size method into the Reweighted Zero-Attracting LMS (RZALMS) algorithm. The improved algorithm, whose step size adjustment is controlled by the instantaneous error, is called Variable step size RZALMS (V-RZALMS). The variable step size leads to yielding smaller steady-state error on the premise of higher convergent speed. Moreover, the sparser the system is, the better the V-RZALMS performs. Three different experiments are implemented to validate the effectiveness of our new algorithm.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

Designs ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 65
Author(s):  
Amritha Kodakkal ◽  
Rajagopal Veramalla ◽  
Narasimha Raju Kuthuri ◽  
Surender Reddy Salkuti
Keyword(s):  
Wind Power ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Normalized Lms

A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

scholarly journals A Novel Self-Interference Cancellation Method Using an Optimized LMS Algorithm in CCFD Systems for a 5G Communication Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3308
Author(s):  
Zeng-You Sun ◽  
Yu-Jie Zhao
Keyword(s):  
Wireless Communication ◽  
Interference Cancellation ◽  
Convergence Speed ◽  
Lms Algorithm ◽  
Full Duplex ◽  
Time Varying ◽  
Least Mean Square ◽  
Step Size ◽  
Channel Tracking ◽  
Step Factor

The Co-frequency Co-time Full Duplex (CCFD) is a key concept in 5G wireless communication networks. The biggest challenge for CCFD wireless communication is the strong self-interference (SI) from near-end transceivers. Aiming at cancelling the SI of near-end transceivers in CCFD systems in the radio frequency (RF) domain, a novel time-varying Least Mean Square (LMS) adaptive filtering algorithm which is based on step-size parameters gradually decrease with time varying called the DTV-LMS algorithm is proposed in this paper. The proposed DTV-LMS algorithm in this paper establishes the non-linear relationship between step factor and the evolved arct-angent function, and using the relationship between the time parameter and error signal correlation value to coordinately control the step factor to be updated. This algorithm maintains a low computational complexity. Simultaneously, the DTV-LMS algorithm can also attain the ideal characteristics, including the interference cancellation ratio (ICR), convergence speed, and channel tracking, so that the SI signal in the RF domain of a full duplex system can be effectively cancelled. The analysis and simulation results show that the ICR in the RF domain of the proposed algorithm is higher than that in the compared algorithms and have a faster convergence speed. At the same time, the channel tracking capability has also been significantly enhanced in CCFD systems.

LMS-Based Notch Filter for AMB Unbalance Compensation Control

2012 ◽  
Vol 614-615 ◽  
pp. 498-503
Author(s):  
Huai Liang Li ◽  
Yu Sheng Wang ◽  
Yang Zhang
Keyword(s):  
Mutual Influence ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Force Balance ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Displacement Signal ◽  
Amb System

The stability and other properties of the active magnetic bearing system influenced by periodic vibration impact with the same frequency of the rotor speed are analyzed. Firstly, on the basis of the periodic displacement signal, the filtering theory of least mean square (LMS) algorithm and the deficiency of LMS algorithm in the application of AMB system, such as frequency mismatch, the relation of step-size and the frequency of displacement signal, and the mutual influence among the step-size length and frequency of the rotor and the PID controller coefficient, etc are studied. And then, a new strategy of variable step-size and real-time switching control with LMS and PID combination to filter the sinusoidal displacement signal to achieve adaptive force balance compensation is proposed. Finally, the feasibility of the new method for the adaptive compensation of the vibration of AMB system is tested by the experimental results.

DCT-BASED VARIABLE STEP SIZE GRIFFITHS' LMS ALGORITHM FOR RANDOM NOISE CANCELLATION IN ECGs

2012 ◽  
Vol 12 (05) ◽  
pp. 1250025
Author(s):  
VEENA N. HEGDE ◽  
RAVISHANKAR DEEKSHIT ◽  
P. S. SATYANARAYANA
Keyword(s):  
Random Noise ◽  
Noise Cancellation ◽  
Lms Algorithm ◽  
Frequency Resolution ◽  
Variable Step Size ◽  
Least Mean Square ◽  
Step Size ◽  
Low Leakage ◽  
Variable Step ◽  
The Time Domain

This paper presents a new random noise cancellation technique for cancelling muscle artifact effects from ECG using ALE in the transformed domain. For this a transform domain variable step size griffith least mean square (TVGLMS) algorithm is proposed. The technique is based on the adaptation of the gradient of the error surface. The method frees both the step size and the gradient from observation noise and reduces the gradient mis-adjustment error. The sluggishness introduced due to the averaging of the gradient in the time domain is overcome by the transformed domain approach. The proposed algorithm uses a discrete cosine transform (DCT)-based signal decomposition due to its improved frequency resolution compared to a discrete Fourier transform (DFT). Furthermore, as the data used symmetrical, DCT usage results in low leakage (bias and variance). The performance of the proposed method has been tested on ECG signals combined with WGN, extracted from MIT database, and compared with several existing techniques like LMS, NLMS, and VGLMS.

An Improved Variable Step Size LMS Algorithm Based on Sigmoid Function

2014 ◽  
Vol 602-605 ◽  
pp. 3593-3596 ◽  
Author(s):  
Li Liu ◽  
Qiong Wang ◽  
Hong Ping Bai
Keyword(s):  
Lms Algorithm ◽  
Variable Step Size ◽  
Sigmoid Function ◽  
Least Mean Square ◽  
Error Performance ◽  
Step Size ◽  
Channel Tracking ◽  
Channel Characteristics ◽  
Variable Step ◽  
Improved Algorithm

LMS (Least Mean Square) algorithm performance is analyzed. Then something has been done on SVSLMS that is an improved variable step size LMS algorithm based on sigmoid function. The improved algorithm has faster convergence rate, stronger channel tracking capability, better anti-noise performance and steady-state error performance compared with SVSLMS, for it makes parameters and change with channel characteristics. Theoretical analysis is the same as computer simulation results, which proves that the performance of the improved algorithm is superior to SVSLMS algorithm.

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