Leaky Least Mean Square Algorithm Research Articles

A Frequency-Domain Output-Constrained Active Noise Control Algorithm Based on an Intuitive Circulant Convolutional Penalty Factor

Due to their computational efficiency, least mean square (LMS)–based algorithms are still widely utilized to achieve optimal control in active noise control (ANC) applications. Real-world implementation of advanced ANC functionalities, such as selective cancellation of frequencies, is nonetheless hampered by complexity trade-offs, especially with computationally-expensive frequency-domain approaches. Prevailing time-domain adaptive algorithms – proposed to alleviate complexities from transformation – continue to incur increased complexities while constraining the magnitude of frequency bins in the time-domain filters. To address existing complexities in time-domain approaches, this paper proposes a circulant convolutional penalty factor that assists the extended leaky filtered-reference LMS (FxLMS) algorithm in achieving frequency constraint without any frequency-domain transform. This circulant convolutional penalty factor is readily determined by methods for designing finite response filters, such as frequency sampling. Additionally, the coordinate descent method is adopted to further reduce the proposed algorithm's computations, significantly increasing its feasibility for implementation on conventional real-time processors. Finally, the numerical simulations performed on the measured primary and secondary paths demonstrate the effectiveness of the proposed algorithm.

Predictive Adaptive Filter for Reducing Total Harmonics Distortion in PV Systems

This paper presents a new method for reducing the total harmonic distortion (THD) of photovoltaic (PV) systems by using an adaptive filter based on a predictive model. Instead of reducing the produced THD at each stage of the PV system, a one-step process is implemented at the end stage. The connection topology of the adaptive filter is similar to normal active and passive filters. The main difference is its ability to adjust the filtering coefficients while others cannot. The proposed method is applied to a single-phase standalone PV system by adopting least mean square (LMS), normalized LMS (NLMS) and leaky LMS algorithms to verify the validity of the proposed method. Various values of filter length and step size are evaluated, and results indicate that the proposed method can reduce THD in the current signal of the PV system significantly by using all of the mentioned algorithms. Different step sizes and filter lengths directly influence the effectiveness of the THD reduction, with small step sizes and long filters being the most effective. Amongst the algorithms, NLMS reduces THD the most, and LMS reaches the peak current value the fastest.

Adaptive Noise Cancellation Techniques for Impedance Cardiography Signal Analysis

Impedance Cardiography (ICG) evaluation facilitates the volume of heart stroke in the sudden cardiac arrest. It is a noninvasive method for measurement of stroke volume, cardiac output monitoring and observing the hemodynamic parameters by changes in the body blood volume. Bloodvolume changes caused due to various physiological processes is extracted in the form of the variations in the impedance of the body segment. In the real time clinical environment during the extraction the ICG signals are influenced with several artifacts.As these artifacts are not stationary in nature, we can’t predict their characteristics. Hence,we developed several hybrid adaptive filtering mechanisms to improve the ICG signals resolution. Least mean square (LMS) algorithm is the basic enhancement technique in the adaptive filtering. However, in the non-stationery situation the LMS algorithm suffers with low rate of convergence and weight drift problems. In this paper we developed some hybrid variantsof LMS algorithm those are Leaky LMS (LLMS) for ICG signal enhancement. More over to progress the convergence rate, filtering capability and to reduce the computational complexity we also developed various sign versions of LLMS algorithms. The sign variants of LLMS algorithms are sign regressor LLMS (SRLLMS), Sign LLMS (SLLMS), and Sign Sign LLMS (SSLLMS). Severaladaptive signal enhancement units (ASEUs) are developed based on adaptive algorithms and performance is evaluated on the real ICG signal taken from MIT-BIT database. To ensure the efficiency of these algorithms, four experiments were performed to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the ASEU associated with SRLLMS performs better in the artifacts filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 9.3388 dBs, 5.7514 dBs, 8.4449 dBs and 8.7358 dBs respectively for SA, RA, MA and EA. Hence, the SRLLMS based ASEUs are more suitable in ICG signal filtering in real time health care sensing systems.

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

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.

Variable Constraint based Least Mean Square algorithm for power system harmonic parameter estimation

Abstract This paper presents the maiden application of a novel signal processing algorithm called Variable Constraint based Least Mean Square (VCLMS) for power system harmonic parameter estimation. The amplitude, phase and frequency of a power signal containing harmonics, sub-harmonics, inter-harmonics are estimated using this algorithm in the presence of white Gaussian noise under simulating environment. Four Least Mean Square (LMS) based algorithms, reported in the literature are considered for judging the comparative performance with the proposed algorithm. These algorithms are applied and tested for both stationary as well as dynamic signals containing harmonics. Practical validation is made with the experimentation of the algorithms with real time data obtained from a solar connected inverter system used for supplying electrical energy during power cut at National Institute of Technology (NIT) Silchar through a power quality analyzer and estimation are performed in MATLAB simulation. Comparison of the results amongst LMS, Normalized LMS, Complex Normalized LMS, Variable Leaky LMS and VCLMS algorithms reveals that proposed VCLMS algorithm is the best in terms of accuracy and computational time.

A Modified Leaky-LMS Algorithm

 Abstract—The leaky least-mean-square (LLMS) algorithm was first proposed to mitigate the drifting problem of the least- mean-square (LMS) algorithm. Though the LLMS algorithm solves this problem, its performance is similar to that of the LMS algorithm. In this paper, we propose an improved version of the LLMS algorithm that brings better performance to the LLMS algorithm and similarly solves the problem of drifting in the LMS algorithm. This better performance is achieved at a negligible increase in the computational complexity. The performance of the proposed algorithm is compared to that of the conventional LLMS algorithm in a system identification and a noise cancellation settings in additive white and correlated, Gaussian and impulsive, noise environments. Index Terms—Leaky least-mean-square, system identification, noise cancellation.

A Leaky RLS Algorithm: Its Optimality and Implementation

A leaky recursive least squares (LRLS) algorithm obtained by a criterion of the ridge regression with the exponential weighting factor was recently proposed by one of the authors. On the other hand, an optimization criterion for improving the method of total least squares (TLS) has been proposed by Chandrasekaran et al. In this work, it is expressed that there is a case where the equation obtained by the criterion of the LRLS algorithm is identical to one obtained by the extended criterion of Chandrasekaran et al. In addition, some implementations of the LRLS filter by using the method for updating the eigendecomposition of rank-one matrix updates, or by using the leaky least mean square (LLMS) algorithm, are introduced to decrease the computational complexity of the LRLS algorithm. Moreover, by means of computer experiments, it is shown that the LRLS and the LLMS algorithms yield more precise estimation parameters than the RLS algorithm when the method of Chandrasekaran et al. is more useful than that of LS and TLS. Besides, it is demonstrated that the LLMS algorithm can be effectively introduced into a noise reduction system for noisy speech signals to support the theoretical results in this work.

LYAPUNOV TUNING OF THE LEAKY LMS ALGORITHM FOR SINGLE-SOURCE, SINGLE-POINT NOISE CANCELLATION

Least-mean square (LMS) algorithms, which are commonly used for adaptive feedforward noise cancellation, have performance issues related to insufficient excitation, non-stationary reference inputs, finite-precision arithmetic, quantisation noise and measurement noise. Such factors cause weight drift and potential instability in the conventional LMS algorithm. Here, we analyse the stability and performance of the leaky LMS algorithm, which is widely used to correct weight drift. A Lyapunov tuning method is developed to find an adaptive leakage parameter and adaptive step size that provide optimum performance and retain stability in the presence of measurement noise on the reference input of known variance. The method accounts for non-persistent excitation conditions and non-stationary reference inputs and requires no a priori knowledge of the reference input signal characteristics other than a lower bound on its magnitude or a minimum signal-to-noise ratio. The Lyapunov tuning method is demonstrated for three candidate adaptive leakage and step size parameter combinations, each of which is a function of the instantaneous measured reference input, measurement noise variance, and/or filter length. These candidates illustrate stability vs performance tradeoffs in the leaky LMS algorithm elicited through the Lyapunov tuning method. The performance of each candidate Lyapunov tuned algorithm is evaluated experimentally in a single source, single-point acoustic noise cancellation system.

Modified leaky LMS algorithm for channel estimation in DS-CDMA systems

A simple adaptive least mean square (LMS) type algorithm for channel estimation is developed based on certain modifications to finite-impulse response (FIR) Wiener filtering. The proposed algorithm is nearly blind since it does not require any training sequence or channel statistics, and it can be implemented using only noise variance knowledge. A condition guaranteeing the convergence of the algorithm and theoretical mean square error (MSE) values are also derived. Computer simulation results demonstrate that the proposed algorithm can yield a smaller MSE than existing techniques, and that its performance is close to that of optimal Wiener filtering.

Application of the leaky extended LMS (XLMS) algorithm in stereophonic acoustic echo cancellation

Analysis of the leaky least mean square (LMS) adaptive algorithm has justified the use of a leakage factor in many applications. In this work, a similar leakage factor is introduced in the two-channel LMS and the extended LMS (XLMS) algorithms for use in stereophonic acoustic echo cancellation. This is compared with the alternative of adding random white noise to the input stereo signals. Simulations and experimental results indicate that introduction of the leakage factor is superior to the direct addition of random white noise. Performance measures used are based on output error and weight error vector norms.