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The harmonic suppression performance of active power filter (APF) under model predictive current control is obviously reduced, when the parameter exists a mismatch between the actual value in system and the nominal value adopted in the control. To suppress the influence of parameter mismatching, a model predictive control based on parameter identification with forgetting factor recursive least square (FFRLS) method is proposed for APF. The digital processing procedure of the FFRLS-based APF controller is detailed analyzed and designed. The identification error of inductance between identification value and the actual value is less to 6%, and the total harmonic distortion of grid current is significantly improved, especially a lower value of actual inductance. Simulation and experimental results show that the proposed method can not only effectively eliminate the influence of parameter mismatch on the control, but also result in better steady-state performance while retaining fast dynamic response. [ABSTRACT FROM AUTHOR]

Focusing on the performance deterioration of nonlinear filtering algorithms in the error-in-variable (EIV) model, a nonlinear version of maximum total correntropy (MTC) algorithm called kernel recursive maximum total correntropy (KRMTC) is proposed in this brief, which constructs a nonlinear relationship based on MTC criterion between inputs and outputs in high-dimensional feature space. Compared with existing kernel adaptive filtering (KAF) algorithms based on traditional mean square error (MSE) and maximum correntropy criteria (MCC), the proposed algorithm can handle nonlinear modeling problems in the EIV model well. In addition, an approximate linear dependency (ALD) criterion is applied to reduce the computational complexity of KRMTC. To verify the feasibility of the proposed algorithm, firstly, the convergence of the KRMTC algorithm under some assumptions is discussed and its local convergence condition is given. Secondly, the proposed algorithm is applied to the Lorenz time-series prediction and wind prediction simulations. Simulation results display that the KRMTC algorithm has better performance than kernel recursive least squares (KRLS) and kernel recursive maximum correntropy (KRMC) algorithms in EIV case. [ABSTRACT FROM AUTHOR]

The direction-of-arrival (DOA) estimation model of this brief is based on deviation compensation model with input noise, and the performance of the traditional Least mean square (LMS) adaptive algorithm shows poor performance. Instead, total least squares (TLS) algorithm is widely used in models which contains input noise. Therefore, we present TLS algorithm for DOA estimation, which is used to update weight coefficient by searching the peak of the spatial spectrum to estimate the direction of the angle of arrival. Due to the unsatisfactory DOA estimation performance on fixed step algorithms, a variable step-size gradient descent total least-squares (VSS-GDTLS) is proposed. The variable step size strategy is derived by applying the instantaneous augmented weight vector and the estimated signal power. Moreover, the convergence of the proposed algorithm is analyzed. Finally, simulation results show the superiority of the VSS-GDTLS algorithm than GDTLS and the other adaptive algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the flexibility of the correntropy that is usually in the form of a single kernel, this brief proposes a novel combinatoric maximum correntropy criterion (C-MCC), where the combinatoric correntropy (C-C) consists of two different single-type kernels. As a new form of correntropy, the C-C has a more generalized form than the traditional single-kernel correntropy, and the corresponding properties and proofs of the C-C are given in this brief. It is noticed that the combined kernel can become a single kernel by changing the combined parameters. Moreover, the adaptive filtering algorithm based on C-MCC is derived and the theoretical performance analysis of the C-MCC algorithm is also provided. Finally, the experimental simulations show that the proposed algorithm has better convergence speed and steady-state performance than MCC-type and other algorithms, and the proposed method of the theoretical analysis of C-MCC agrees well with simulation results. [ABSTRACT FROM AUTHOR]

Multiple strokes are the main factors affecting the safe and stable operation of power equipment. As the core lightning protection equipment, arresters are also frequently thermally collapsed due to multiple strokes. This paper investigates thermal effects of polymer-housed metal-oxide surge arrester (PMOSA) under multiple strokes. The comparative experiments of PMOSA lightning impulse are carried out through multiple strokes of experiment platform, including continuous lightning stroke and multiple strokes. Furthermore, the temperature distribution of the housing surface is obtained by infrared imager for thermal effect analysis. Subsequently, some typical DC parameters are tested to explore the association of impulse aging and thermal effects of the arrester under multiple strokes. Finally, a thermal-electric coupling model (TECM) is constructed to investigate the influence of some factors on the temperature of PMOSA from the perspective of materials and structure. The research in this paper can provide a theory reference and brief data support for improving the heat dissipation performance of arresters under multiple strokes based on structural optimization design and material modification. [ABSTRACT FROM AUTHOR]

To enhance the filtering accuracy of the traditional sign subband adaptive filter (SSAF) algorithm and its individual-weighting-factors variant, this brief proposes the logarithmic hyperbolic cosine normalized subband adaptive filter algorithm (LHCNSAF) and its sparsity-aware version through minimizing the LHC cost function. As the combination of mean-square-error and mean-absolute-error criterion, the presented LHCNSAF demonstrates higher filtering accuracy under white Gaussian background noise and impulse noise environments than SSAF-type algorithms, with almost no increase in computational complexity. To tackle the trade-off between filtering accuracy and convergence behavior caused by the fixed step-size, an effective variable step-size scheme is also devised based on the transient model of proposed algorithms through minimizing the mean-square deviation (MSD) at each iteration with some reasonable assumptions. Additionally, the stability and complexity analysis are also provided. Finally, the computer simulations certify the proposed algorithms possess good performance in terms of convergence speed, steady-state error, and tracking capability via contrasting with the considering algorithms under system identification application. [ABSTRACT FROM AUTHOR]

In the errors-in-variables (EIV) model, the total least squares (TLS) algorithm has shown good convergence performance. In this model, the input and output signals are simultaneously infected with noise. However, when the output signal is contaminated with impulse noise, the convergence performance of TLS will decrease or even diverge. In order to settle this problem, we propose a hyperbolic secant total least squares (HSTLS) algorithm through adopting the hyperbolic secant function as the cost function. The HSTLS algorithm shows better performance in non-Gaussian noise environment. At the same time, in order to deal with the contradiction between the algorithm’s convergence speed and steady-state deviation with the fixed step size, this brief also provides a new variable step size strategy. The improved variable step size HSTLS (VHSTLS) algorithm shows better performance in simulation. Furthermore, to reduce steady-state deviation of the HSTLS algorithm in sparse systems, a series of improved HSTLS algorithms based on the sparse criterion have also been proposed, and they also have excellent convergence performance. [ABSTRACT FROM AUTHOR]

An adaptive least mean M-estimate algorithm for censored regression (CR-LMM) is presented for the robust parameter estimation of the censored regression system. To correct the bias produced by censored observation, the estimated error derived from the probit regression model is employed to construct an M-estimate cost function. It can expel the adverse impact of the impulsive noise and is solved by the unconstrained optimization method. Furthermore, the robust variable step-size (VSS) strategy, which is also predicted on the robust cost function, is also utilized to improve the convergence performance of the proposed CR-LMM algorithm, i.e., convergence speed and steady-state mean square deviation. The condition which guarantees the CR-LMM algorithm stability is obtained by analyzing the convergence in the mean and mean-square sense, and the theoretical steady-state result is also derived. Computer simulations in system identification scenarios are carried out to demonstrate that the proposed algorithms are superior to the existing algorithms in the impulsive environment with different background noise and the theoretical results are verified. [ABSTRACT FROM AUTHOR]

The adaptive filtering algorithm based on the maximum correntropy criterion (MCC) is very effective in suppressing non-Gaussian noises and therefore attracts widespread attentions. At present, some works have been done for study the convergence and steady-state performance analysis of the MCC algorithm, but its transient performance analysis is still an open problem. To provide a comprehensive theoretical foundation for the MCC algorithm, we propose a method for transient performance analysis based on moment generating function (MGF). Since this method can efficiently calculate the expected value of the exponential term in the iterative update equation, it can avoid the discrepancies caused by introducing some approximation methods such as Taylor expansions in the analysis process. To date, there is no precedent for using this method to analyze the transient performance of the MCC algorithm. In addition, the steady-state performance and stability conditions of the MCC algorithm are discussed based on this method. Finally, the proposed analytical method is applied to the system identification problem, and the results show that the theoretical analysis results are agree well with the Monte Carlo simulation results. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]

The minimum error entropy (MEE) criterion has been receiving increasing attention over the minimum mean square error (MMSE) criterion in non-Gaussian noise distribution, because it accounts for all higher order moments. In this brief, a novel MEE algorithm was proposed by using information theoretical learning concepts and the widely linear (augmented) complex domain modelling approaches for enhanced power system frequency estimation. The proposed augmented complex minimum error entropy (ACMEE) utilizes the complex-valued voltage signal, modeled by the Clarke’s $\mathbf {\mathrm {\alpha }}\mathbf {\mathrm {\beta }}$ transformation, which used all second-order statistical information for processing of non-circular complex-valued voltage signals. Performance degradation of the MMSE criterion in impulsive noise environments can be overcome by MEE adaptation scheme due to the higher order moments imbedded in its cost function. Therefore, the proposed ACMEE algorithm is able to achieve robust frequency estimation for unbalanced conditions and under the interference of measurement noises. The effectiveness of the ACMEE frequency estimation technique is verified through simulation studies of synthetic signals and experimental data. [ABSTRACT FROM AUTHOR]

Subband adaptive filter has been widely applied to process correlated input signals due to its decorrelation property. However, the performance of the subband adaptive filter algorithm will be drastically degraded in the case of both the input and output signals are contaminated with noise. To tackle this problem, this brief proposes a total least squares normalized subband adaptive filter (TLS-NSAF) algorithm,which is different from bias-compensated schemes. The proposed algorithm is derived by employing the Rayleigh quotient as the cost function and the gradient steepest descent method. The local mean stability and computational complexity of the proposed algorithm are also analyzed. Simulation results demonstrate that the TLS-NSAF algorithm has better performance in comparison with previous algorithms. [ABSTRACT FROM AUTHOR]

When the disturbance of impulsive noise exists in the multitask network, the convergence behavior of the traditional multitask diffusion affine projection (AP) algorithm (MD-APA) is significantly suppressed. To alleviate this problem, in this brief, a robust MD-APA is proposed based on maximum correntropy criterion (MCC), which is called MD-APMCC algorithm. Due to the shortcomings of the fixed kernel width, this brief adopts a robust adaptive kernel width strategy to increase the estimation behavior of the MD-APMCC algorithm. Besides, the convergence behavior of MD-APMCC algorithm is studied to derive the convergence range of step-size and the theoretical steady-state mean square deviation (MSD) of the whole network. The simulation verification demonstrates that the proposed MD-APMCC algorithm appears better estimation behavior than MD-APA and MD-APSA for multitask distributed estimation under impulsive noise interference, and the theoretical steady-state MSD of MD-APMCC algorithm is obtained through mean square analysis, which has been well verified by several simulations. [ABSTRACT FROM AUTHOR]

To thoroughly exploit the decorrelation property of the traditional sign subband adaptive filter (SSAF), an individual weighting factors SSAF (IWF-SSAF) was presented. However, when the input of the adaptive filter is polluted by white Gaussian noise, the IWF-SSAF will produce estimation bias for unknown system identification. Thus, this brief proposes the bias-compensated IWF-SSAF (BC-IWF-SSAF) for resolving the above trouble. Especially, the unbiased criterion is applied to obtain compensation term for the conventional IWF-SSAF to alleviate the effect of noisy input. The proposed BC-IWF-SSAF both retains stability of impulse noise and decreases the estimated bias. The stable condition of the proposed BC-IWF-SSAF is achieved resorting to Price’s theorem with some reasonable assumptions and theorems. In addition, the complexity is given as well. Finally, simulations confirm the superiority of the proposed BC-IWF-SSAF algorithm. [ABSTRACT FROM AUTHOR]

The constant kernel width of generalized maximum correntropy criteria (GMCC) has arisen that the steady-state error and convergence speed can be mutually exclusive. To solve this problem, this brief proposes the variable kernel width (VKW) GMCC algorithm. Actually, due to the censored problem, the output data value beyond the limit of the recording device can not be well observed. In this case, we further developed a variable kernel width GMCC algorithm based on censored regression (CR-VKWGMCC). Simulation results show that the proposed CR-VKWGMCC algorithm has excellent performance in both Gaussian and non Gaussian noise. [ABSTRACT FROM AUTHOR]

In this brief, considering the dramatically deteriorating convergence behavior of the diffusion gradient-descent total least squares (DGDTLS) algorithm in the error-in-variables (EIV) model containing impulsive noise, a robust diffusion total least mean M-estimate (DTLMM) algorithm is proposed, which incorporates the M-estimate function (MF). The proposed algorithm significantly enhances the robustness of the DGDTLS algorithm in suppressing impulse noise. Under the impulse noise conditions, the mean condition and steady-state mean-square behaviors of the DTLMM algorithm are evaluated in detail. Algorithm simulations under different conditions are executed. The simulation results prove the robustness of the proposed algorithms and the consistency between the theoretical value and the simulated value. [ABSTRACT FROM AUTHOR]

In this brief, an improved maximum correntropy criterion subband adaptive filter (MCC-SAF) algorithm is presented, which has excellent performance for alleviating the effect of impulsive noise and noisy input. Though the MCC-SAF algorithm performs well in the face of impulsive interference due to the advantage of the correntropy-based cost function, it yields estimated error when the system input is disturbed by noise. Profiting from the property of unbiased criterion, we propose a bias-compensated MCC-SAF algorithm (BC-MCC-SAF) by introducing a biased compensated term into the MCC-SAF algorithm. Besides, the computational complexity of the BC-MCC-SAF is investigated. Simulation results for system identification under various input signals have illustrated that the presented algorithm both retains robust achievement for the impulsive interference circumstance and achieves lower stable state error and brilliant tracking ability for noisy input. [ABSTRACT FROM AUTHOR]

In practical applications, the impulse responses (IRs) of some network echo paths are blocksparse (BS), while the traditional proportionate and zero attraction algorithms do not consider the prior sparsity of the BS system, so they do not perform well in the block-sparse system identification (BSSI). In addition, most of the current BS filtering algorithms are based on the assumption of Gaussian noise, so the performance will deteriorate seriously in the background of impulse noise. To overcome the shortcoming, we use the mixed $l_{2,1} $ norm of the filter weight vector to fully tap the sparsity of the BS system, and combine the anti impulse noise characteristic of the M-estimate function to design and derive the BS proportionate normalized least mean M-estimate (BSPNLMM) algorithm from the perspective of basis pursuit (BP), which well realizes the BSSI in the presence of impulse noise. Then, we analyze the mean performance of the BSPNLMM algorithm in detail and give the stable step size bound. Finally, the superiority of the proposed BSPNLMM algorithm is verified by numerical simulations [ABSTRACT FROM AUTHOR]

Non-Gaussian colored input signal and censored observation are encountered frequently in many practical applications of adaptive signal processing. Conventional adaptive algorithms will face convergence performance degradation in such cases. To address the issue, the affine projection algorithm for censored regression (CR-APA) is proposed in this brief. The censored thresholds and the variance of the background noise are regarded as the prior knowledge to compensate for the bias caused by censored observation. By theoretical analysis, the range of the step size is derived to guarantee the stabilization of the CR-APA. Besides, the method of evolving projection order is employed to improve the convergence performance of the CR-APA. Computer simulations in system identification and echo cancellation applications are performed to demonstrate the better convergence performance of the proposed algorithms in censored data processing over competing algorithms, and to verify the improvement of the CR-APA by the method of evolving projection order. The proposed CR-APA and CR-EAPA can be used in engineering applications to make up for the hardware. [ABSTRACT FROM AUTHOR]

A new testing method (oscillating wave method) has been proposed as a solution to the problem of short maintenance time in the traction power supply system, which could complete tests of winding and insulation via one connection and improve the maintenance efficiency of the transformer. Through the method being proved highly sensitive to winding faults, the variation under different winding states, however, remains unclear. This article aims to analyze oscillating wave signatures in the occurrence of axial displacement (AD). First, the modeling method will be proposed that can simulate oscillating wave signatures in different winding states. Then, the time-domain feature and the new proposed frequency-domain feature are applied to analyze the fault extent and location of AD fault. The results show that this model accurately simulates oscillating wave signatures. Moreover, the time-domain feature [Euclidean distance (ED)] is closely related to fault extent. The combined use of the time-domain feature (ED) and the frequency-domain feature (W-Energy entropy in Z-Score) is effective in the identification of fault location. These features, therefore, can provide the reference for winding state evaluation. [ABSTRACT FROM AUTHOR]

Recently, maximum correntropy criterion (MCC) has been widely and successfully used in robust signal processing and machine learning, in which the correntropy is maximized instead of minimizing the popular mean square error (MSE) to improve the robustness with respect to outliers or impulsive noises. A lot of efforts have been devoted to derive different adaptive algorithms under MCC, but to date, little insight has been gained as to how the MCC solution will be influenced by outliers. In this paper, we investigate this problem and our focus is mainly on the parameter estimation of a simple linear errors-in-variables (EIVs) model with scalar variables. Under some conditions, we derive an upper bound on the absolute value of the estimation error and show that the MCC solution can get very close to the true value of the unknown parameter even with arbitrarily large outliers in both the input and output variables. Illustrative examples are provided to verify and clarify the theory. [ABSTRACT FROM AUTHOR]

In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution. [ABSTRACT FROM AUTHOR]

This brief proposes a polynomial bias-compensated adaptive filtering algorithm for sparse system identification under minimum error entropy criterion, which outperforms in a sparse environment with noisy input and impulsive output noise. In previous studies, the minimum error entropy criterion (MEE) has been widely used in lots of works to develop adaptive filtering algorithms, which show better performance than the traditional algorithms under impulsive noise. However, the MEE algorithm not only has the essential assumption of no-noise input, but also loses the ability to identify the sparse system. To address above two issues, a bias-compensated vector is introduced into the previous traditional MEE algorithm and propose a bias-compensated adaptive filtering algorithm under minimum error entropy criterion named BCMEE algorithm, which compensates the bias caused by the input noise. By taking advantage of the capability of the sparse penalty term in sparse system identification, an attempt has been made to design a polynomial sparse BCMEE algorithm, named PZA-BCMEE algorithm. Simulation results prove the excellent performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

We consider the DOA estimation problem by using adaptive nulling antenna technology and bias-compensated adaptive filtering framework. The adaptive nulling antenna array consists of one reference antenna element and multiple auxiliary antenna elements, which forms nulls in certain directions. By defining the reciprocal of array pattern as the spatial spectrum, the DOA estimation problem is considered as the peak searching for it. In this paper, we present an efficient bias-compensated adaptive filtering algorithm for DOA estimation based on the adaptive nulling array. First of all, a new noise-free mean square error (NF-MSE) cost function associated with the adaptive nulling array is provided. Secondly, the proposed algorithm, called variable step-size bias-compensated least mean square (VSS-BC-LMS) algorithm, can be derived by minimizing the NF-MSE. Due to the bias compensation, the bias of the estimated weight vector caused by noises is canceled. More importantly, in order to resist the unsatisfactory DOA estimation performance when using the fixed step-size, a variable step size scheme is introduced. And then, its mean stability, steady state mean square performance and computational complexity are discussed. Additionally, we also explore a noise variance estimation method for calculating the bias term. Simulation results indicate that the proposed algorithm achieves an improved DOA estimation performance compared with other adaptive methods. [ABSTRACT FROM AUTHOR]

In this brief, an affine projection maximum correntropy criterion with correntropy induced metric (APMCCCIM) algorithm is proposed for robust sparse adaptive filtering, and it is derived by using the cost function based on affine projection maximum correntropy criterion and correntropy induced metric to eliminate the adverse effects of impulsive noise on filter weight update in sparse systems. In order to further improve the convergence speed and steady-state misalignment of the proposed APMCCCIM algorithm, the variable step-size method is incorporated into the APMCCCIM algorithm. Hence, the variable step-size APMCCCIM (VSS-APMCCCIM) algorithm is presented. Besides, the computational complexity and the range of step-size of the proposed APMCCCIM algorithm are analyzed. Simulation results show that the proposed APMCCCIM and VSS-APMCCCIM algorithms have faster convergence speed and lower steady-state misalignment for sparse system identification and echo cancellation scenarios in the impulsive noise environments. [ABSTRACT FROM AUTHOR]

The maximum correntropy criterion (MCC) algorithm has attracted much attention due to its capability of combating impulsive noise. However, its performance depends on choice of the kernel width, which is a hard issue. Several variable kernel width schemes based on various error functions have been proposed to address this problem. Nevertheless, these methods may not provide an optimal kernel width because they do not contain any knowledge of the background noise that actually has influence on the optimization of the kernel width. This brief proposes an improved variable kernel width MCC algorithm, which is derived by minimizing the squared deviation at each iteration. We also design a reset mechanism for the proposed algorithm to improve its tracking capability when the estimated vector encounters a sudden change. Simulations for system identification and echo cancellation scenarios show that the proposed scheme outperforms other variable kernel width algorithms. [ABSTRACT FROM AUTHOR]

Correntropy is an efficient tool for analyzing higher order statistical moments in non-Gaussian noise environments. Correntropy has been used with real, complex, and quaternion data. However, there is no literature studying the correntropy with geometric algebra data. The geometric algebra has been widely used in the engineering applications including the signal processing and image processing. From the probabilistic view, this brief presents a novel geometric algebra correntropy (GAC) which represents the similarity measure between two random geometric algebra (multivector valued) variables. Then, we develop the adaptive filter based on the maximum GAC criteria (MGACC), which is robust against the non-Gaussian noise. Simulation is conducted to verify the advantages of the proposed adaptive algorithm under the non-Gaussian environment. [ABSTRACT FROM AUTHOR]

Correntropy has recently attracted great attention because of its robustness to non-Gaussian noise. Inspired by this robustness property, a spline adaptive filtering algorithm based on the maximum correntropy criterion (SAF-MCC) has been proposed. However, the steady-state convergence performance of the SAF-MCC has not been studied. This brief presents a steady-state performance analysis of the SAF-MCC for Gaussian and non-Gaussian noise. We derive and discuss the theoretical excess mean square error (EMSE) under these cases. Monte Carlo simulations are presented to corroborate the theoretical performance analysis results developed in this brief. [ABSTRACT FROM AUTHOR]

The reweighted zero-attracting least mean square (RZA-LMS) algorithm has good performance in sparse system identification. However, the convergence performance of the RZA-LMS algorithm degrades in the impulsive noise environment. In this brief, a reweighted zero-attracting least mean M-estimate (RZA-LMM) algorithm is proposed to address the issue. The RZA-LMM algorithm employs the M-estimate cost function with a sparsity-aware penalty term and is derived by using the unconstrained minimization method. In order to further enhance the convergence speed and reduce the steady-state error of the RZA-LMM algorithm, a variable step-size reweighted zero-attracting least mean M-estimate (VSSRZA-LMM) algorithm is proposed. The variable step-size scheme, which adopts the M-estimate cost function with a sparsity-aware penalty term and a constrained term of the step-size, is obtained by using the constrained minimization method. Besides, the mean-square stability is analyzed to obtain the range of the step-size, which guarantees the stabilization of the proposed algorithms. The computer simulation results show that the proposed RZA-LMM algorithm achieves better convergence performance than the RZA-LMS algorithm, and the proposed VSSRZA-LMM algorithm exhibits better convergence performance in terms of convergence speed and steady-state normalized mean square deviation (NMSD) than the RZA-LMM algorithm for sparse system identification in the impulsive noise environment. [ABSTRACT FROM AUTHOR]

The errors-in-variables (EIV) model is widely used in linear systems where both input and output signals are contaminated with noise. For the parameter estimation in the EIV model, the adaptive filtering algorithm using total least squares (TLS) approach has shown better performance than classical least squares (LS) approach. However, the TLS approach which is based on minimizing the mean squared total error may be irrational in the presence of impulsive noise. To address this problem, a novel robust adaptive algorithm, named as the total least mean M-estimate (TLMM) algorithm, is proposed in this brief, which combines the advantages of TLS approach and M-estimate function. In addition, to further improve the performance of the TLMM algorithm, its variable step-size (VSS) version has been developed. Moreover, we carry out the local stability analysis and the computational complexity analysis. Simulation results show that the proposed algorithms outperform some well-known algorithms. [ABSTRACT FROM AUTHOR]

In this brief, an affine-projection-like M-estimate (APLM) algorithm is proposed for robust adaptive filtering. To eliminate the adverse effects of impulsive noise in case of the impulse interference environment on the filter weight updates. The proposed APLM algorithm uses a robust cost function based on M-estimate and is derived by using the unconstrained minimization method. More importantly, the APLM algorithm has lower computational complexity than the M-estimate affine projection algorithm, since the direct or indirect inversion of the input signal matrix does not need to be calculated. In order to further improve the performance of the APLM algorithm, namely convergence speed and steady-state misalignment, the convex combination of the APLM (C-APLM) algorithm is presented. Simulation results verify that the proposed APLM and C-APLM algorithms are effective in system identification and echo cancellation scenarios. It also demonstrates that the C-APLM algorithm improves the filter performance in terms of the convergence speed and the normalized mean squared deviation in the presence of impulse noise. [ABSTRACT FROM AUTHOR]

The shrinkage linear complex-valued least mean squares (SL-CLMS) algorithm with a variable step size overcomes the conflicting issue between fast convergence and low steady-state misalignment. To the best of our knowledge, the theoretical performance analysis of the SL-CLMS algorithm has not been presented yet. This letter focuses on the theoretical analysis of the excess mean square error transient and steady-state performance of the SL-CLMS algorithm. Simulation results obtained for identification scenarios show a good match with the analytical results. [ABSTRACT FROM AUTHOR]

To further improve the performance of the variable step size continuous mixed ${p}$ -norm (VSS-CMPN) adaptive filtering algorithm in the presence of impulsive noise, a generalized VSS-CMPN algorithm is proposed in this brief. Instead of assuming the probability density-like function ${\lambda (p)}$ to be uniform, a linear function is proposed for ${\lambda (p)}$ to control the mixture of various error norms. The influence of the selection of the regulating factor (slope of the linear function) is discussed. Besides, the computational complexity as well as the mean-square convergence analysis is presented. Simulations conducted in the system identification scenario demonstrate the superiority of the proposed algorithm over known algorithms. [ABSTRACT FROM AUTHOR]

Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequentially, online detection method that can process streaming data is preferred. In this paper, we formulate a distributed online OCSVM for anomaly detection over networks and get a decentralized cost function. To get the decentralized implementation without transmitting the original data, we use a random approximate function to replace the kernel function. Furthermore, to find an appropriate approximate dimension, we add a sparse constraint into the decentralized cost function to get another one. Then we minimize these two cost functions by stochastic gradient descent and derive two distributed algorithms. Some theoretical analysis and experiments are performed to show the effectiveness of the proposed algorithms. Experimental results on both synthetic and real datasets reveal that both of the proposed algorithms achieve low misdetection rates and high true positive rates. Compared with other state-of-the-art anomaly detection methods, the proposed distributed algorithms not only show good anomaly detection performance, but also require relatively short running time and low CPU memory consumption. [ABSTRACT FROM AUTHOR]

This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios. [ABSTRACT FROM AUTHOR]

The diffusion least-mean ${p}$ -power algorithm is presented for distributed estimation in impulsive noise environments, which aims to minimize the ${p}$ -norm of the error. However, it suffers from slow convergence rate. In this brief, we propose a diffusion normalized least-mean ${p}$ -power algorithm (DNLMP), motivated by the normalized-based algorithms. To further improve the performance of the DNLMP algorithm, a robust DNLMP (RDNLMP) algorithm is developed for distributed estimation. The RDNLMP algorithm considers the error signal in normalization factor, and therefore can diminish the significance of outliers under impulsive noise environments. Moreover, the steady-state analysis of the RDNLMP algorithm is provided. Both performance analysis and numerical simulations are given to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

One of the main problems facing beamforming in smart antenna system is the $ {\alpha }$ -stable noise. To address this problem, a novel algorithm, named the recursive continuous logarithmic mixed ${p}$ -norm (RCLMP) algorithm, which employs a logarithmic cost, is proposed in this brief. The proposed algorithm combines the logarithmic ${p}$ -norms ${1}{\leq } p \leq 2$ which does not need the parameter selection and prior knowledge of ${\alpha }$ -stable noise, and exhibits good robustness against ${\alpha }$ -stable noise. Moreover, we show some ${H}^{{\infty }}$ norm bounds for the proposed algorithm. Simulation results show that the RCLMP algorithm outperforms the existing algorithms in terms of interference rejection capability and estimation accuracy. [ABSTRACT FROM AUTHOR]

In this letter, a novel diffusion Volterra (DV) algorithm is proposed for distributed in-network system identification in the presence of $\alpha$ -stable noise. The proposed algorithm is based on the logarithmic least mean $p$ th-power criterion, which makes it robust against impulsive interferences, at the price of increased complexity. To overcome this shortcoming, we further develop the diffusion interpolated Volterra algorithm, which provides computational savings and good performance in comparison with the DV algorithm. Simulations results show that the proposed adaptive algorithms achieve better performance than the state-of-the-art approaches for distributed nonlinear system identification in impulsive noise. [ABSTRACT FROM AUTHOR]

Nonlinear similarity measures defined in kernel space, such as correntropy, can extract higher order statistics of data and offer potentially significant performance improvement over their linear counterparts especially in non Gaussian signal processing and machine learning. In this paper, we propose a new similarity measure in kernel space, called the kernel risk-sensitive loss (KRSL), and provide some important properties. We apply the KRSL to adaptive filtering and investigate the robustness, and then develop the MKRSL algorithm and analyze the mean square convergence performance. Compared with correntropy, the KRSL can offer a more efficient performance surface, thereby enabling a gradient-based method to achieve faster convergence speed and higher accuracy while still maintaining the robustness to outliers. Theoretical analysis results and superior performance of the new algorithm are confirmed by simulation. [ABSTRACT FROM PUBLISHER]

As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel, which is, of course, not always the best choice. In this paper, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel, and present some important properties. We further propose the generalized maximum correntropy criterion (GMCC) and apply it to adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is derived, and the stability problem and steady-state performance are studied. We show that the proposed algorithm is very stable and can achieve zero probability of divergence (POD). Simulation results confirm the theoretical expectations and demonstrate the desirable performance of the new algorithm. [ABSTRACT FROM AUTHOR]

Compared with the least-mean-square algorithm, the least mean $p$th power algorithm shows a better robustness performance against impulsive noises such as the $\alpha$-stable noises. However, it still exhibits slow convergence rate and high kernel misadjustment. To overcome this drawback, a novel recursive logarithmic least mean $p$th (RLLMP) algorithm is proposed for the Volterra system identification under $\alpha$-stable noise environments. Instead of minimizing the $p$th power, the new algorithm aims to minimize the $p$th logarithmic cost, which makes it more robust against impulsive interferences. Furthermore, to enhance tracking performance, an improved variable forgetting factor (IVFF) algorithm (IVFF-RLLMP) is proposed, which is based on the robust estimation of outliers. Simulation results are presented to demonstrate the improved performance of the RLLMP and IVFF-RLLMP. [ABSTRACT FROM AUTHOR]

A bias-compensated normalized subband adaptive filter (BC-NSAF) algorithm is proposed for system identification. In the proposed algorithm, a bias-compensation vector is derived to eliminate the bias caused by the noisy input signals. To estimate the input noise variance, a new estimation method is proposed, which does not require the input–output variance ratio in advance. Simulation results show that the proposed algorithm obtains better convergence performance than the existing algorithms in the presence of noisy input signals. [ABSTRACT FROM PUBLISHER]

This paper presents a novel modular recurrent neural network based on the pipelined architecture (PRWNN) to reduce the computational complexity and improve the performance of the recurrent wavelet neural network (RWNN). The PRWNN inherits the modular architectures of the pipelined recurrent neural network proposed by Haykin and Li and is made up of a number of RWNN modules that are interconnected in a chained form. Since those modules of the PRWNN can be simultaneously performed in a pipelined parallelism fashion, this would lead to a crucial improvement of computational efficiency. Furthermore, owing to the cascade interconnection of dynamic modules, the performance of the PRWNN can be further enhanced. An adaptive gradient algorithm based on the real-time recurrent learning is derived to suit for the modular PRWNN. Simulation examples are given to evaluate the effectiveness of the PRWNN model on the identification of nonlinear dynamic systems and analysis of sunspot number time series. According to simulation results, it is clearly shown that the PRWNN provides impressive better performance in comparison with the single RWNN model. [ABSTRACT FROM PUBLISHER]