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Summary: This paper proposes a fractional delay (FD) Newton structure for Lagrangian interpolation in single phase grid integrated photovoltaic (PV) system. The structure has been designed for achieving shunt compensation and as a phase locked loop circuit for synchronization. The designed controller performs multiple tasks of feeding the active power to meet the load demand and simultaneously providing reactive power compensation, power factor improvement, and compensating other power quality voltage and load current harmonics. Further, synchronization poses a severe challenge in integrating the PV power electronic converters to the grid. The FD filter is used to discretize the continuous time signal at any time instant using interpolation algorithm employing the non‐integral value of the sampling rate delays. The FD delay filter is made adaptive and self‐learning by using least mean square algorithm. The designed synchronization technique meets the objective of faithfully tracking the phase of the grid voltage under all grid disturbances such as under voltage, overvoltage, frequency variations, and phase angle disturbances in a precise manner. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

The standard proportionate normalized least mean square (PNLMS) adaptive algorithm suffers from convergence performance limitation due to a constant step-size during the convergence period. In this paper, a switching-based variable step-size PNLMS is proposed to improve the convergence performance in sparse system identification. To adjust the step-size, the convergence performance of PNLMS is first analysed in the statistical sense and by exploiting the analysis, a switching-based method is then proposed, which brings about a fast convergence towards the desired steady-state mean-square weight deviation. The step-size reduces during the convergence period in a few steps, while in the case of abrupt change in the system impulse response, the step-size increases to its initial value. A sub-band version of the proposed adaptive algorithm is further proposed for highly correlated input signals. Simulation results confirm the superiority of the proposed full-band and sub-band algorithms in terms of convergence performance compared to some competing adaptive algorithms. [ABSTRACT FROM AUTHOR]

The random finite set (RFS) approach for multi-target tracking is widely researched because it has a rigorous theoretical basis. However, many prior parameters such as the clutter density, survival probability and detection probability of the target, pruning threshold, merging threshold, initial state of the birth object and its error covariance matrix are required in the standard RFS-based filters. In real application scenes, it is difficult to obtain these prior parameters. To address this problem, an adaptive marginal multi-target Bayes filter without the need for clutter density is proposed. This filter obviates the need for prior clutter density and survival probability. Instead of using the prior initial states of newborn targets and their error covariance matrices, it uses two scans of observations to generate the initial states of potential birth targets and their error covariance matrices according to the least squares technique. Simulation results reveal that the proposed adaptive filter has smaller OSPA and OSPA(2) errors as well as less cardinality error than the adaptive RFS-based filters. The OSPA and OSPA(2) errors have been reduced by more than 20% compared to those of the adaptive RFS-based filters. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes. [ABSTRACT FROM AUTHOR]

The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor. [ABSTRACT FROM AUTHOR]

Active noise cancellation of audio signals, which has been the subject of a substantial quantity of research in the past, remains a significant obstacle in many aspects of acoustic signal processing. In a natural environment, the system’s parameters are in constant flux. Consequently, we shall investigate the filtered-x least mean square (FxLMS) algorithm. The adaptive filter is used as a controller to generate an anti-noise signal in the feedforward FxLMS algorithm, which only addressed narrowband and broadband noise. The feedback FxLMS algorithm, on the other hand, enhances the algorithm’s convergence, but cannot eliminate the effect of residual noise’s uncorrelated disturbance. However, both the correlated and uncorrelated disturbances must be under user control. Therefore, a combination of the feedback and feed-forward FxLMS algorithms was employed, which consists of two portions for estimating the primary disturbances and the uncorrelated disturbances. A variable step size algorithm has been employed to attain a balance between system stability and control efficiency. In Akhtar’s method, an external adaptive filter was applied to the feed-forward system in order to estimate a more precise error signal and enhance the performance of the systems. These improvements will improve the algorithm’s ability to control both primary and uncorrelated noise. [ABSTRACT FROM AUTHOR]

A new strategy for the detection of rotor electrical asymmetry in wind turbine doubly fed induction generators is presented. In comparison with the previous work, the major novelty of this article is the addition of an adaptive noise cancelation block in the process of detecting the rotor winding fault. This work demonstrates an improvement in the signal-to-noise ratio using robust least mean squares filtering of the wavelet transform method. In the new strategy, robust least mean squares filtering of wavelet transform is used for the first block of fault detection before the fault estimation block. The proposed strategy is verified with the experimental data measured on doubly fed induction generator test rigs and real wind turbine. A comparative study is carried out based on experimental measurements (stator current and power output) under the steady-state and variable-speed operation representative of real wind turbine operations for both healthy and faulty generator conditions. [ABSTRACT FROM AUTHOR]

Active noise control is an expanding field that requires a suitable synthesis of secondary perturbations. Unfortunately, most schemes for noise cancelling do not take into account that the input signal that drives the adaptive filter can be noisy. In this Letter, it is theoretically shown that noise perturbations in the excitation data degrade the performance of the standard filtered-x least mean squares (FX-LMS) algorithm. Furthermore, a method that compensates such an issue is devised, and a first-order stochastic analysis of the resulting algorithm is performed. The results reveal that the proposed scheme outperforms the standard FX-LMS algorithm, even when the variance of the additive noise in the input is not accurately estimated. [ABSTRACT FROM AUTHOR]

Filter design in signal processing field plays a vital role in achieving low power dissipation, which is essential for portable gadgets. This paper proposes an effective flexible FIR filter structure, which is adaptive and utilizes multiply-accumulate (MAC) core. Most common algorithm for filter coefficient optimization includes least mean square (LMS) and recursive least square (RLS). Though the performance of the recursive least square (RLS) algorithm is superior as compared to the least mean square (LMS); because of higher arithmetic complexity in design, it has not been preferred for real time applications. The fundamental filter has used a LMS based tapped delay line filter, which is practically a feasible choice for adaptive filtering algorithm in order to attain lesser computation. In the proposed work, the adjustable coefficient filters using an optimized LMS approach has been implemented for the utilization of determining the unexplored system. The filter tap considered here is a 32-tap and its analysis and synthesis has been carried out using hardware description language (HDL) programming and synthesized in field programmable gate array (FPGA) devices. The placement and post routing design has offered good performance in terms of utilized resources. The implemented filter architecture requires 80% reduction in resources and has enhanced the clock frequency by about five times when examined with the reported architecture. [ABSTRACT FROM AUTHOR]

In the framework of the maximum a posteriori estimation, the present study proposes the nonparametric probabilistic least mean square (NPLMS) adaptive filter for the estimation of an unknown parameter vector from noisy data. The NPLMS combines parameter space and signal space by combining the prior knowledge of the probability distribution of the process with the evidence existing in the signal. Taking advantage of kernel density estimation to estimate the prior distribution, the NPLMS is robust against the Gaussian and non-Gaussian noises. To achieve this, some of the intermediate estimations are buffered and then used to estimate the prior distribution. Despite the bias-compensated algorithms, there is no need to estimate the input noise variance. Theoretical analysis of the NPLMS is derived. In addition, a variable step-size version of NPLMS is provided to reduce the steady-state error. Simulation results in the system identification and prediction show the acceptable performance of the NPLMS in the noisy stationary and non-stationary environments against the bias-compensated and normalized LMS algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the state estimation problem of linear discrete-time time-varying stochastic systems with unknown inputs (UIs). It is shown that the globally optimal unbiased minimum-variance filters may not satisfy the minimum-variance property, and hence they cannot eliminate noises appropriately. If this is the case, the well-known Kalman filter may give a better solution, which however may also not be the best one due to that the imbedded unknown input model may not be practical. To remedy the filtering degradation problem, a robust filter named as the KFLMS, which has good noise rejection property for such systems, is developed in this paper, where the UI estimates are obtained by using least mean square algorithm and the state estimation is achieved via the previous proposed two-stage Kalman filtering approach. Numerical examples are provided to show the effectiveness of the proposed results. Specifically, simulation results illustrate the goodness of the new method in the sense of lower root mean square error and better noise rejection property. [ABSTRACT FROM AUTHOR]

Abstract Adaptive filters are useful solutions for system identification problem where an optimization problem is used to formulated the estimation of the unknown model coefficients. The nonnegativity constraint is one of the most frequently used constraint which can be imposed to avoid physically unreasonable solutions and to comply with physical characteristics. In this letter, we propose a new variant of non-negative least mean square (NNLMS) that has a less mean square error (MSE) value and faster convergence rate. We provide both mean weight behavior and transient excess mean-square error analysis for proposed algorithm. Simulation results validate the theoretical analysis and show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Additive noise distributions can be divided into three types: Gaussian, super- and sub-Gaussian. The existing algorithms for adaptive filtering do not provide a better performance than the least mean square (LMS) method for the super- and sub-Gaussian noise simultaneously. For example, the maximum correntropy criterion performs better (worse) than the LMS method for super-Gaussian (sub-Gaussian) noise, whereas the least mean fourth performs better (worse) than the LMS method for sub-Gaussian (super-Gaussian) noise. We propose a criterion for switching between sub- and super- Gaussian additive noise, that could be used to assess whether the error signal had a sub- or super-Gaussian profile, and thus determine which algorithm would work best in the iterative process. Simulations demonstrate that the switching criterion helps the proposed switching algorithm to produce a better performance than the LMS algorithm for sub and super-Gaussian noise simultaneously. [ABSTRACT FROM AUTHOR]

The paper presents a new model of noise cancellation using cascading of cascaded LMS adaptive filters. The model has a combination of ‘2N+1’ LMS filters for N-stage of adaptive noise cancellation. First LMS filter works as a basic noise canceller, next two work as 1st stage of noise canceller using a cascaded form of LMS filters known as LMS Block-1, and all others have the same arrangement as LMS Block-1 known as LMS Block-2, LMS Block-3, …, LMS Block-N. The LMS Block has a two-stage noise reduction of the additive noise or interference. LMS Block-1 is cascaded noise canceller, determines and reduces noise again after reduction in noise from 1st LMS filter. The analysis and simulation model gives the responses of noise cancellation like error signal, output signal and signal-to-noise ratio with respect to step sizes, filter lengths and initial weight of filters. This paper also shows the simulation of cascade-cascade LMS adaptive noise cancellation for two stages (N=2) and gives the higher signal-to-noise ratio at Nth stage with respect to previous stages. It is the next novel point of this paper that no other elements are present in the cascade-cascade LMS adaptive noise cancellation rather than LMS filters as noise canceller. [ABSTRACT FROM AUTHOR]

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques. [ABSTRACT FROM AUTHOR]

• Built an active noise control window on the top- hung window frame in a real dormitory room. • Investigate how the noise control performance of this ANC system is affected by controllable system variations. • Figure out the optimal system setup. • Investigate the sensitivity of the ANC window system over the secondary path variation caused by varying window opening status. Urban noise mitigation experiment through active noise control window is not commonly carried out in in situ urban setting. This paper outlines our experimental settings of performing active noise control across the open aperture of the top-hung window frame in a dormitory room. A multi-channel adaptive filtered-x least mean square method is implemented in real-time. Experiments and analyses are carried out to investigate how the noise reduction performance of this active noise control window system is affected by the number of reference microphones, the distance between reference microphones and secondary sources, and the distributions of error microphones under practical restrictions of the real system implementation. Variation of the secondary path estimation is another important consideration in active noise control system design. Real-time experiments are carried out to investigate the sensitivity of the ANC window system over the secondary path variation caused by varying window opening status, and whether a generalised secondary path measured in one window opening setting can be applied to other window opening conditions. [ABSTRACT FROM AUTHOR]

A multitap adaptive filter with analog least mean-square (ALMS) loop is proposed in this paper for effective and low complexity self-interference cancellation implemented as part of the radio frequency frontend in a full duplex transceiver. Comprehensive analyses of the ALMS loop’s behaviors at both micro and macroscales are presented for a wireless communication system with single carrier signaling. It is revealed that there is always an irreducible residual interference due to the cyclostationary property of the transmitted signal. The interference suppression ratio (ISR) lower bound is derived accordingly, which can be used as a design rule for determining the ALMS loop parameter. Stationary analysis shows that the convergence speed and achievable ISR of the ALMS loop are determined by the loop gain and the autocorrelation function of the transmitted signal. The interference channel modeling error with the adaptive filter also accounts for part of the residual interference power. These theoretical findings are verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

The fractional tap-length least mean square adaptive algorithm exhibits robustness and low complexity in adaptive filter design. This algorithm employs one parameter $$\gamma $$ , the tap-length adaptation step size, to balance convergence rate and steady-state tap-length fluctuation. From the viewpoint of threshold parameter, we justify that a time-varying $$\gamma (n)$$ with large value at transient stage and small value at steady stage, instead of a fixed one, can provide both fast convergence rate and small fluctuation. Then, one time-varying strategy for $$\gamma (n)$$ is suggested, where the parameter is adjusted by the difference between squared output error and squared segmented estimation error, and is limited by a sigmoid function. This strategy is motivated by the recognition that such difference indicates transient or steady state. [ABSTRACT FROM AUTHOR]

Objective Heart rate monitoring using wrist type photoplethysmographic (PPG) signals is getting popularity because of construction simplicity and low cost of wearable devices. The task becomes very difficult due to the presence of various motion artifacts. The objective is to develop algorithms to reduce the effect of motion artifacts and thus obtain accurate heart rate estimation. Methods Proposed heart rate estimation scheme utilizes both time and frequency domain analyses. Unlike conventional single stage adaptive filter, multi-stage cascaded adaptive filtering is introduced by using three channel accelerometer data to reduce the effect of motion artifacts. Both recursive least squares (RLS) and least mean squares (LMS) adaptive filters are tested. Moreover, singular spectrum analysis (SSA) is employed to obtain improved spectral peak tracking. The outputs from the filter block and SSA operation are logically combined and used for spectral domain heart rate estimation. Finally, a tracking algorithm is incorporated considering neighbouring estimates. Results The proposed method provides an average absolute error of 1.16 beat per minute (BPM) with a standard deviation of 1.74 BPM while tested on publicly available database consisting of recordings from 12 subjects during physical activities. Conclusion It is found that the proposed method provides consistently better heart rate estimation performance in comparison to that recently reported by TROIKA, JOSS, SPECTRAP and COMB methods. Significance The proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach and thus feasible for implementing in wearable devices to monitor heart rate for fitness and clinical purpose. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is able to measure sparse signals with fewer measurements than Nyquist rate. The reconstruction of sparse signals in CS is an underdetermined problem. For realistic applications, the sparsity information may not be available, the measurements are used to be disturbed by noise, and efficient hardware realization is important. Existing CS reconstruction algorithms are tradeoff between noise tolerance and low complexity. In this paper, we propose a novel reconstruction algorithm called stochastic gradient pursuit (SGP). The stochastic gradient approach is applied to replace least square process of well-known orthogonal matching pursuit (OMP) algorithm. Since stochastic gradient approach iterates toward a minimum mean square error solution, proposed algorithm shows robustness against noise. Furthermore, proposed algorithm inherits the low-complexity property of OMP and shows feasibility of hardware implementation. The SGP algorithm can achieve 36% higher success rate as well as 27% less complexity than OMP when SNR is 20 dB. Finally, we implement the algorithm with designed hardware sharing architecture in TSMC 90 nm technology. The postlayout result shows that the core size is only 1.08 mm 2 at 150 MHz operation frequency. Compared with state-of-the-art ASIC designs, the proposed engine achieves 29% higher throughput-to-area ratio. [ABSTRACT FROM AUTHOR]

In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixed-point arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. [ABSTRACT FROM AUTHOR]

Recently a new normalized least mean square algorithm has been proposed by minimizing the summation of the squared Euclidean norms of the changes between the weight vectors to be updated and the past weight vector. The resultant algorithm exhibits noise resilience in that they prevent the adaptive filter from fluctuating around an optimal solution, but its convergence behavior has not been studied in detail. Thus, we first apply the constrained criterion to an affine projection algorithm (APA) for identifying a highly noisy system by reusing weight vectors. Since the performance of the APA declines under low signal-to-noise ratio (SNR) conditions, this approach is more effective for decreasing the steady-state mean-square deviation (MSD). Then, we analyze the convergence behavior of the proposed APA theoretically using energy conservation arguments. The experimental results show that the proposed theoretical results agree well with the simulation results. [ABSTRACT FROM AUTHOR]

A mixed l p,0-regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS-based algorithms deteriorates when both input and output are contaminated by noise (the error-in-variables problem). We propose an l p,0-RTLS algorithm to handle group sparse system identification with errors-in-variables. The proposed algorithm is an RLS-like solution that utilizes l p,0-regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l p,0-regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The main interest in adaptive filters continues to grow as they begin to find practical applications in areas such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal-processing applications. The work presented in this paper focuses on optimizing most popular adaptive filtering algorithms namely Least Mean Square (LMS) algorithm, Normalized Least Mean Square (NLMS) and Rec ursive least Squares (RLS) by using genetic optimizer approach. The tap-length are updated with the three adaptive algorithms according to the value of mean square error based on genetic style. The simulation results for noise cancellation in speech enhancement demonstrate the good performance of the proposed algorithm in attenuating the noise with less hardware resources complexity. It is a nice tradeoff between hardware complexity, SNR ratio and the convergence speed. [ABSTRACT FROM AUTHOR]

Abstract: The normalized least mean squares (NLMS) and recursive least squares (RLS) algorithms are widely used for adaptive filtering. Interestingly, the NLMS algorithm has been shown to be strictly optimal in the sense of filtering, whereas the forgetting factor RLS algorithm has not been clearly related to a solution to the filtering problem. This paper describes a method for further optimizing the solutions to the ordinary filtering problem over an assumed system model set and a predetermined norm weight set. The extended filtering problem offers a framework for constructing a unified view of adaptive algorithms for finite impulse response (FIR) filters. The framework enables a discussion of the relationships among the NLMS algorithm, the forgetting factor RLS algorithm, and the filter over the common parameter space, and facilitates the development of new fast adaptive algorithms that outperform the existing algorithms, such as the NLMS and the fast RLS algorithms. The validity of the discussion based on the framework is verified using numerical examples. [Copyright &y& Elsevier]

This work presents a complementary broadband noise reduction scheme for hearing aid applications. It is designed to attenuate uncorrelated and small-correlation-length acoustic noise with controlled speech distortion. Noisy speech signals are pre-processed by the proposed strategy before being subjected to an existing narrowband noise reduction system. The clean speech signal is estimated by a convex combination of the unprocessed speech signal and the output of a linear predictor. The convex combination coefficient is adjusted to provide noise suppression while avoiding significant unvoiced utterance distortions. The proposed method is optimized to minimize speech mean-square prediction-error. A low-cost adaptive implementation is proposed and compared to the conventional adaptive linear predictor showing an improved performance, as predicted by theory. Four different objective quality measures and subjective assessment performed by normal hearing volunteers indicate that the combined use of the proposed technique with a narrowband noise reduction system consistently improves speech quality for a range of signal to noise ratios. Low-cost digital hearing aids that make use of the conventional adaptive predictor for broadband noise reduction can be easily modified to incorporate the new proposal with a minimum amount of extra computational resources. [ABSTRACT FROM AUTHOR]

• A forward scattering detection scheme based on LMS algorithm for AUV intruder was proposed. A cumulative index was defined for detection. • Arriving time and intensity on different depths in three tracks were given. Nearly 10 dB aberration was extracted. The active detection of autonomous underwater vehicles (AUVs) is a challenging task because the echoes from targets are seriously weak under a monostatic configuration. In this work, a forward scattering detection scheme was developed for the active sensing of AUV intruders. A linear frequency modulation pulse was transmitted periodically for the acquisition of multipath propagation responses in a harbor environment. The least mean square algorithm after matched filtering was employed for direct blast suppression. A cumulative index was defined for detection on the basis of the error sequence of the adaptive filter. Sea experiments using an AUV as an intruder in crossing events were conducted to demonstrate the ability of the proposed scheme. The time of each crossing event was determined. The detection scheme was found to have a detection capability with a low source level. The performance was determined by the signal-to-direct blast ratio, which was correlated with the source–target–receiver geometry. [ABSTRACT FROM AUTHOR]

• To overcome the tradeoff between the low steady state error and the fast convergence speed of the GA-LMS adaptive filter, we propose the CGA-LMS adaptive filter. • To verify the advantage of the proposed CGA-LMS adaptive filter, we give the steady-state performance of analysis the CGA-LMS adaptive filter. • To speed up the overall convergence rate of the GA-LMS adaptive filter, a novel geometric algebra combination algorithm with the geometric algebra instantaneous transfer strategy is proposed. • To process the noncircular 3D and 4D signals, the CWL-GA-LMS and CWL-GA-LMS algorithms are investigated. • We compare the learning performance of the proposed CGA-LMS and CGA-LMS-TS algorithms with that of the GA-LMS. Recently, the geometric-algebra theory and the geometric-algebra based adaptive filters have been applied to numerous applications, such as 3D wind speed, computer vision and fusion prediction of dynamic pressure. However, similar to the real-valued adaptive filter, the geometric-algebra based adaptive filters also have the tradeoff problem between the low steady state error and the fast convergence speed. To overcome this shortcoming, this paper proposes a novel geometric algebra adaptive algorithm by convexly combining two geometric algebra least mean square algorithms with two different step sizes. Afterwards, this paper gives a detail steady state performance analysis of the CGA-LMS algorithm by using the geometric algebra theory. Moreover, to address the phenomenon that the slow filter may lag considerably behind the fast filter, which slows down the overall convergence of the combined geometric-algebra filter, we proposed a novel instantaneous transfer strategy, further leading to the CGA-LMS algorithm with transfer strategy (CGA-LMS-TS). To process the noncircular 3D and 4D signals, we have proposed the convex combination of widely linear GA-LMS (CWL-GA-LMS) algorithm. The CWL-GA-LMS with transfer strategy (CWL-GA-LMS) is also investigated. Simulation results for multivector-valued input are presented to verify the performance of the proposed algorithms and the correctness of the performance analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an interferometry method for refractive index determination in membranes of fuel cells. This technique is based on the use of an improved laser heterodyne interferometer. The photocurrents of the avalanche photodiodes, resulting from reflected beams of the optical head, are led to the signal conditioner and digital signal processing sections. The optical path difference between the target and reference paths is fixed, and as a result, the phase shift is calculated in terms of the refractive index shift. In addition, nonlinearity of this system is analyzed and modeled with different neural networks and adaptive filter algorithms. For neural networks, the radial basis function (RBF), the multi-layer perceptron (MLP), and the stacked generalization method are simulated. In adaptive filter algorithms, the least mean square (LMS), the normalized least mean square (NLMS), the recursive least squares (RLS), and the affine projection algorithm (APA) are applied. The simulation results indicate that the RLS method is faster and contains minimum mean square error (MSE) compared to the other approaches. Also, comparison between two main approaches shows that the nonlinearity of refractive index determination can be effectively modeled with adaptive filter algorithms. [Copyright &y& Elsevier]

This paper presents an alternative technique for the adaptive control of power electronic converter circuits. Specific attention is given to the adaptive control of a dc–dc converter. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). The method is computationally efficient and based around a dichotomous coordinate descent (DCD) algorithm. The DCD-recursive least squares (RLS) algorithm has been employed as the adaptive PEF to reduce the computational complexity of existing RLS algorithms for efficient hardware implementation. Results show that the DCD-RLS is able to improve the dynamic performance and convergence rate of the adaptive gains (filter taps) within the controller. In turn, this yields a significant improvement in the overall dynamic performance of the closed-loop control system, particularly in the event of abrupt parameter changes. The proposed controller uses an adaptive proportional–derivative+integral (PD +I) structure which, alongside the DCD algorithm, offers an effective substitute to a conventional proportional–integral–derivative (PID) controller. The nonadaptive integral controller (+I), introduced in the feedback loop, increases the excitation of the filter tap weight and ensures good regulation. The approach results in a fast adaptive controller with self-loop compensation. This is required to minimize the prediction error signal and, in turn, minimize the voltage error signal in the loop by automatically calculating the optimal pole locations. The prediction error signal is further minimized through a second-stage FIR filter (adaptation gain stage). This ensures that the adaptive gains converge to their optimal value. This paper presents detailed simulation analysis and experimental validation on a prototype synchronous dc–dc buck converter. The experimental results clearly demonstrate the superior dynamic performance and voltage regulation compared to conventional PID and adaptive LMS control schemes, with only a modest increase in the computational burden to the microprocessor. [ABSTRACT FROM AUTHOR]

Two proportionate affine projection sign algorithms (APSAs) are proposed for network echo cancellation (NEC) applications where the impulse response is often real-valued with sparse coefficients and long filter length. The proposed proportionate-type algorithms can achieve fast convergence and low steady-state misalignment by adopting a proportionate regularization matrix to the APSA. Benefiting from the characteristics of l_1-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the proportionate least mean squares (PNLMS) algorithm and the robust proportionate affine projection algorithm (Robust PAPA). The new algorithms also achieve much faster convergence rate in sparse impulse responses than the original APSA and the normalized sign algorithm (NSA). The new algorithms are robust to all types of NEC impulse response with different sparseness without the need to change parameters or estimate the sparseness of the impulse response. The computational complexity of the new algorithms is lower than the affine projection algorithm (APA) family due to the elimination of the matrix inversion. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new variable regularized least-squares (VR-LS) algorithm by recursively constructing a weighting scalar of the regularized least-squares (LS) cost function. Since the recursive LS (RLS) algorithm provides the best performances by all of VR-LS algorithms, the design objective of the weighting scalar is chosen such that equivalent optimality is ensured between one-step-ahead cost functions of the RLS and of the VR-LS algorithm. The proposed VR-LS algorithm functions similarly as the RLS with uncorrelated inputs; however, this is not the case with colored (correlated) inputs. Therefore, a conventional filtering technique is applied to both on the inputs and on the desired signals so as to obtain whitened inputs. This enables the proposed algorithm handle the case of correlated inputs. [Copyright &y& Elsevier]