Mean Fourth Adaptive Algorithm Research Articles

Elimination of EOG signals from raw EEG signals using step size based recursive least squares- least mean fourth adaptive algorithm

A novel Step Size based Recursive least squares- Least mean fourth(SSRL) adaptive algorithm is proposed as a noise canceller, in this paper, to eliminate the Ocular artifacts(OAs) from the recorded raw EEG signals. Here, both second and fourth-order power optimization algorithms have been used to eliminate the OAs from raw EEG signals. The Reference signals, such as vertical Electrooculogram and horizontal Electrooculogram signals, have been recorded separately.FIR filter has been used to process the reference signals by updating filter coefficients using the proposed SSRL algorithm. Thereby, true EEG signals are obtained by subtracting the processed signals from the raw signals. Moreover, a mathematical model for the mean square deviation analysis is proposed for the SSRL algorithm, which provides better results compared to the conventional methods.

Mixed step size normalized least mean fourth adaptive algorithm for artifact elimination from raw EEG signals

In this paper, a novel algorithm is proposed to eliminate the ocular artifacts (OAs) using mixed step size normalized least mean fourth adaptive algorithm from the raw EEG signals. Here, the fourth-order power optimization technique is used to eliminate the OAs effectively. The reference signals, such as vertical electrooculogram and horizontal electrooculogram, are recorded separately. Thereby, these signals are processed by the FIR filter, whose coefficients are adaptively updated at each iteration by using the proposed algorithm. The processed signals are subtracted from the raw EEG signals in order to obtain clean EEG signals. In addition, the mathematical model for the mean square deviation analysis is provided and compared with conventional methods like combined step size normalized least mean squares and variable parameter normalized mixed norm adaptive algorithm. From the simulation results, it is found that the proposed algorithm exhibits better performance in terms of minimum mean square deviation.

Efficient reduction of PLI in ECG signal using new variable step size least mean fourth adaptive algorithm

It is very important in remote cardiac diagnosis to extract pure ECG signal from the contaminated recordings of the signal. When recording the ECG signal in the laboratory, the signal is affected by numerous artifacts. Varies artifacts generally degrades the signal quality are PLI, EM, MA and EM. In addition to these, the channel noise also added when transmitting signal from remote location to diagnosis center for analyzing the signal. There are several approaches are used to reduce the noise present in the ECG signal. From the literature it is proven that compared to non adaptive filters, adaptive filters play vital role to trace the random changes in the corrupted signals. In this paper, we proposed efficient Variable step size leaky least mean fourth algorithm and its sign versions for reducing the complexity. These algorithms shows that it gives low steady state error due to least mean fourth and fast convergence rate that is it tracks the input signal quickly because of its variable step size is high at initial iterations of signal compared to the LMS algorithm. The performance of the algorithm is evaluated using SNR, frequency spectrum, MSE, misadjustment and convergence characteristics.

Leaky least mean fourth adaptive algorithm

In this work, a leakage-based variant of the least mean fourth (LMF) algorithm, the leaky least mean fourth (LLMF) algorithm, is proposed. This algorithm will help mitigate the weight drift problem experienced in the conventional LMF algorithm. The main aim of this work is to derive the LLMF adaptive algorithm, analyse its convergence behaviour, and examine its performance in different noise environments. Furthermore, the tracking and transient analysis of the proposed LLMF algorithm are carried out using the energy-conservation relation framework. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and show improved performance obtained through the use of LLMF over the conventional LMF algorithm in a weight drift scenario.

Analysis of the Sign Regressor Least Mean Fourth Adaptive Algorithm

A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. A sufficient condition for the convergence in the mean of the SRLMF algorithm is derived. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment, and consequently an optimum value of the step-size is obtained. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex.

A Mean-Square Stability Analysis of the Least Mean Fourth Adaptive Algorithm

This paper presents a new convergence analysis of the least mean fourth (LMF) adaptive algorithm, in the mean square sense. The analysis improves previous results, in that it is valid for non-Gaussian noise distributions and explicitly shows the dependence of algorithm stability on the initial conditions of the weights. Analytical expressions are derived presenting the relationship between the step size, the initial weight error vector, and mean-square stability. The analysis assumes a white zero-mean Gaussian reference signal and an independent, identically distributed (i.i.d.) measurement noise with any even probability density function (pdf). It has been shown by Nascimento and Bermudez [Probability of Divergence for the Least-Mean Fourth (LMF) Algorithm, IEEE Transactions on Signal Processing, vol 54, no. 4, pp. 1376-1385, Apr. 2006] that the LMF algorithm is not mean-square stable for reference signals whose pdfs have infinite support. However, the probability of divergence as a function of the step size value tends to rise abruptly only when it moves past a given threshold. Our analysis provides a simple (and yet precise) estimate of the region of quick rise in the probability of divergence. Hence, the present analysis is useful for predicting algorithm instability in most practical applications.

Least mean fourth(LMF) lattice adaptive algorithm

The least mean fourth adaptive algorithm (LMF), recently reported by Walach and Widrow (1984 WALACH , E. , and WIDROW , B. , 1984 , Least mean fourth (LMF) adaptive algorithm and its family. I.E.E.E. Trans. Inf. Theory , 30 , 275 – 283 .[Crossref] , [Google Scholar]) is studied in combination with the lattice structure. It is found that this combination, ‘LMF-lattice’, as an adaptive spectral estimator has better spectral properties like, resolvability and spectral fit to block data spectrum, than the least mean square (LMS) lattice algorithm for the same speed of convergence.