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The diffusion least-mean-square (dLMS) algorithms have attracted much attention owing to its robustness for distributed estimation problems. However, the performance of such algorithms may change when they are implemented for acoustic echo cancellation (AEC) systems. To overcome this problem, a leaky dLMS algorithm is proposed in this work, which is characterized by its numerical stability and small steady-state error for noisy speech signals. Then, we perform some stability and convergence analyses of the proposed algorithm for Gaussian inputs and verify the theory results by simulations. As an added contribution in this paper, we further develop a new variable leakage factor (VLF) strategy for the leaky dLMS algorithm to overcome the parameter selection of adaptation. Finally, implementations of the proposed algorithms in the context of system identification and stereophonic AEC (SAEC) network are performed. Simulation results illustrate that the leaky diffusion algorithms achieve improved performance as compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

As a robust adaptation criterion, the maximum correntropy criterion (MCC) has gained increased attention due to its successful application in the adaptation, especially in nonlinear and non-Gaussian situations. In this paper, the second-order Volterra (SOV) filter based on MCC is derived, which is called MCC-SOV. It combines the advantages of the SOV filter and MCC. However, similar to the conventional adaptive algorithm, the proposed MCC-SOV filter has a tradeoff between convergence rate and steady-state error. In order to solve this problem, we present a combination of the MCC-SOV filter by combining Volterra kernels, which is called CK-MCC-SOV. In addition, a weight transfer method is applied to improve the performance of the proposed filter in terms of the convergence rate and the steady-state error. Finally, simulations are carried out to demonstrate the advantages of the proposed filters. [ABSTRACT FROM AUTHOR]

Variable step size norm-constrained adaptive filtering algorithms are proposed in the paper. A variable step size is derived by minimizing the variance of the noise-free a posterior error. Thus, the update equation can obtain a reasonable step size at each iteration. Due to the introduction of variable step size, the proposed algorithms based on the constrained conditions of $$L_{1}$$ and $$L_{0}$$ norm have a significant advantage that the convergence rate is faster than some well-known algorithms in the sparse system. The simulation results illustrate the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Recently, the diffusion sign subband adaptive filtering (DSSAF) algorithm has attracted great attention because of its robustness against the impulsive noise and its decorrelation power to the correlated input signals. However, the DSSAF converges slowly when the system to be identified is sparse. In order to solve the problem of slow convergence rate in the sparse system, this paper proposes a diffusion proportionate sign subband adaptive filtering (DPSSAF) algorithm by minimizing $$L_{1}$$ -norm of the subband a posteriori error vector subject to weighted constraint for each node in the diffusion network. The proposed DPSSAF algorithm is robust against the impulsive noise in the sparse system. To further improve the performance of the DPSSAF in the very sparse system, an efficient version (EDPSSAF) based on $$L_{0}$$ -norm is obtained, which can better measure the sparseness level of the unknown system with high sparsity. Simulation results are presented to illustrate the good performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a robust set-membership affine projection algorithm with coefficient vector reuse (RSM-APA-CVR) for high background noise environment. In the proposed algorithm, the sum of the squared $$L_{2}$$ norms of the differences between the updated weight vector and past weight vectors is minimized and a new robust error bound is designed. Simulation results in acoustic echo cancellation context show that the proposed algorithm has faster convergence rate and smaller steady-state misalignment as compared to the conventional RSM-APA. [ABSTRACT FROM AUTHOR]

To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF-SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step-size IWF-SSAF filter and a small step-size one; and the second algorithm is to obtain a time-varying step size for the IWF-SSAF by combining a large step size and a small one. The minimization of the sum of the l1-norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step-size IWF-SSAF filter to the small step-size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms. [ABSTRACT FROM AUTHOR]

For high background noise environment, an improved affine projection subband adaptive filter (IAPSAF) is proposed, which is derived by minimizing the sum of the squared Euclidean norms of the differences between the updated tap-weight vector and past tap-weight vectors. Due to the use of the past tap-weight vectors, the IAPSAF can achieve lower steady-state misalignment relative to the improved multiband-structured subband adaptive filter (IMSAF). Furthermore, a combined step size IAPSAF (CSS-IAPSAF) is developed to improve the performance of the IAPSAF. Simulation results in an acoustic echo cancellation (AEC) context show that the IAPSAF achieves lower steady-state misalignment than the existing algorithms, such as normalized subband adaptive filter (NSAF), IMSAF, and improved NSAF (INSAF). The improvement of the CSS-IAPSAF is also demonstrated in terms of convergence rate and steady-state misalignment. [ABSTRACT FROM AUTHOR]

Multi-view clustering has been a significant research problem in unsupervised clustering in recent years and has important applications in computer vision, data mining and other fields. To explore more clustering information from different views and improve performance, researchers have developed a variety of multi-view clustering algorithms by applying the same L 1 or L 2 , 1 norm constraints for different views, which essentially assumes that the errors satisfy the characteristics described by the corresponding norms. It is clear that such assumption is not always satisfied in the real-world datasets. Therefore, the performance of existing algorithms can decrease under the complex noise that may be contained in the data. To solve the above problem, we introduce an information theoretic learning-based metric into a multi-view model to address noise. First, we propose a new mixture correntropy (MC) metric. Then, we propose a MC based robust multi-view K-means clustering (MC-RMVKM) method by applying the MC to an existing model. Furthermore, an efficient iterative algorithm is derived to solve the joint MC-RMVKM learning problem. Extensive results on real-world multi-view data show that the MC-based algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

A new diffusion sign subband adaptive filtering algorithm with an individual weighting factor (IWF-DSSAF) for each subband is proposed for distributed estimation in the impulsive noise environment. Since the inherent decorrelating property of subband adaptive filtering is fully used, the proposed algorithm obtains better performance in terms of convergence rate and tracking capability as compared with the diffusion sign subband adaptive filtering (DSSAF) algorithm. In addition, the stability analysis of the IWF-DSSAF algorithm is performed based on Price's theorem. After that, in order to obtain a faster convergence rate in sparse distributed system identification, the improved proportionate IWF-DSSAF algorithm is proposed, in which a gain distribution matrix is incorporated into the IWF-DSSAF algorithm. Finally, simulations are carried out in the distributed system identification context. The results of simulations demonstrate that the proposed algorithms achieve better convergence performance than their counterparts. [ABSTRACT FROM AUTHOR]

In acoustic echo cancellation (AEC), the sparseness of impulse responses can vary over time or/and context. For such scenario, the proportionate normalized subband adaptive filter (PNSAF) and μ -law (MPNSAF) algorithms suffer from performance deterioration. To this end, we propose their sparseness-measured versions by incorporating the estimated sparseness into the PNSAF and MPNSAF algorithms, respectively, which can adapt to the sparseness variation of impulse responses. In addition, based on the energy conservation argument, we provide a unified formula to predict the steady-state mean-square performance of any PNSAF algorithm, which is also supported by simulations. Simulation results in AEC have shown that the proposed algorithms not only exhibit faster convergence rate than their competitors in sparse, quasi-sparse and dispersive environments, but also are robust to the variation in the sparseness of impulse responses. [ABSTRACT FROM AUTHOR]

The least mean kurtosis (LMK) algorithm has been successfully applied to linear system identification. It outperforms the conventional least mean square method for Gaussian and non-Gaussian noises. The purpose of this work is to apply the LMK algorithm to active noise control (ANC) systems, i.e., to develop a filtered-x LMK (FxLMK) algorithm. The proposed FxLMK algorithm is robust against the attenuation of various noise interferences in ANC system. To further improve the noise reduction performance, a recursive sampled variance method is incorporated into the FxLMK algorithm, resulting in an improved FxLMK (IFxLMK) algorithm without significantly increasing the computational burden. Simulations in the various noise inputs show the effectiveness of the proposed algorithms. Moreover, the traction substation noise control problem is considered in our simulations, and an overall system scheme and simulation studies are conducted. Simulation results demonstrate that the IFxLMK algorithm can improve the convergence rate of adaptive filter and improve the performance of noise reduction. [ABSTRACT FROM AUTHOR]

This paper presents a new proportionate normalized subband adaptive filter (PNSAF) algorithm, called the IAF-PNSAF, which assigns an individual activation factor for each filter coefficient instead of a common one for all filter coefficients as in the standard PNSAF algorithm. This algorithm achieves an improved performance in terms of the convergence rate and tracking capability for identifying a highly sparse system. Moreover, to overcome the trade-off of the fixed-step-size IAF-PNSAF algorithm between the fast convergence rate and small steady-state misalignment, its adaptive combination version is proposed. Simulation results for identifying the impulse response with high sparseness have demonstrated that the proposed algorithms outperform their counterparts. [ABSTRACT FROM AUTHOR]

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments. [ABSTRACT FROM AUTHOR]

To improve the robustness of subband adaptive filter (SAF) against impulsive interferences, we propose two modified SAF algorithms with an individual scale function for each subband, which are derived by maximizing correntropy-based cost function and minimizing logarithm-based cost function, respectively, called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the subband scale functions can sharply drop the step size, which eliminate the influence of outliers on the tap-weight vector update. Therefore, the proposed algorithms are robust against impulsive interferences and exhibit the faster convergence rate and better tracking capability than the sign SAF (SSAF) algorithm. Besides, in impulse-free interference environments, the proposed algorithms achieve similar convergence performance as the normalized SAF (NSAF) algorithm. Simulation results have demonstrated the performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies. [ABSTRACT FROM AUTHOR]

Proportionate-type normalized subband adaptive filter (PNSAF-type) algorithms are very attractive choices for echo cancellation. To further obtain both fast convergence rate and low steady-state error, in this paper, a variable step size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is proposed by minimizing the square of the noise-free a posterior subband error signals. A noniterative shrinkage method is used to recover the noise-free a priori subband error signals from the noisy subband error signals. Significantly, the proposed VSS strategy can be applied to any other PNSAF-type algorithm, since it is independent of the proportionate principles. Simulation results in the context of acoustic echo cancellation have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have optimal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. • We provide the analysis of the transient and steady-state behaviors of the proposed algorithm. • Numerical simulations illustrate the superior performance of the VRP-WL-CNLMS algorithm and the validity of the theoretical analysis. To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have optimal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. The proposed algorithm can adaptively change the regularized parameter by exploiting a time-varying parameter that is obtained via making the power of noise-free a posteriori error minimum. A proper estimated method is provided to compute the power of the measured noise when the noise is unknown, and the moving-average method is employed to update the regularized parameter for avoiding large fluctuations. Then we provide the analysis of the transient and steady-state (TAS) behaviors of the proposed algorithm. Simulation results with different input signals illustrate that the VRP-WL-CNLMS algorithm has better advantages than other algorithms, and verify the theoretical validity of TAS analysis of the proposed algorithm in the system identification (SI) environment. Finally, the experimental results of wind prediction show that the predicted value of the proposed algorithm has a smaller error value with the original signal than the WL-CNLMS algorithm and can predict the signal better that can support the superiority of the proposed algorithm as well. [ABSTRACT FROM AUTHOR]

Motivated by the need for robust frequency estimation for both balanced and unbalanced power system conditions and the fact that most of the existing techniques perform poorly in the presence of external interferences, a more noise‐resilient widely linear least mean kurtosis (WL‐LMK) algorithm is proposed that uses both the magnitude and phase angle errors for online estimation of three‐phase power system frequency. The proposed method makes use of the full second‐order information within the complex‐valued system voltage through Clarke's αβ transformation and the concept of augmented complex statistics and WL modelling. The effectiveness of the proposed scheme is verified through simulation studies of synthetic signals and experimental data. [ABSTRACT FROM AUTHOR]

The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC) is robust to heavy-tailed non-Gaussian noise. However, the approximate linear measurement equation obtained by statistical linearization technique may be not accurate enough since it only uses prior information. In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods. Different from the existing UKF algorithms, we only need to perform one-time unscented transformation in each filtering cycle, reducing the execution time of algorithm. Then a nonlinear enhancement model is constructed to handle predictions and observations simultaneously, which will be included in the cost function of robust MCC. In the process of iterative solution, thanks to the latest iteration values which are used to update the measurement information, RS-IMCC-UKF has more accurate results compared with traditional filters. In order to avoid non-positive definite characteristic in covariance matrix and the inverse operation in ill-conditioned matrix, the hyperbolic QR decomposition and Moore–Penrose pseudo-inversion are introduced to improve the numerical stability of the algorithm. Finally, a target tracking is modeled to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new sign subband adaptive filter (SSAF) algorithm with an individual-weighting-factor (IWF) for each subband, instead of a common weighting factor in the original SSAF algorithm, called the IWF-SSAF. Each individual weighting factor only depends on the corresponding subband input signal power. Compared with the SSAF algorithm, the proposed approach fully utilizes the inherent decorrelating property of subband adaptive filter for colored inputs, leading to a better convergence performance. After that, to further enhance the performance of the IWF-SSAF in a sparse system, an improved proportionate IWF-SSAF (IWF-IPSSAF) algorithm is proposed. The proposed algorithms not only inherit the good robustness of sign algorithm against impulsive interferences, but also obtain a significant improvement in the performance as compared to their counterparts (i.e., SSAF and IPSSAF), in terms of the convergence rate and tracking capability. Besides, the IWF-IPSSAF algorithm has faster convergence rate than the IWF-SSAF algorithm for sparse impulse responses. Finally, the performances of two proposed algorithms are demonstrated in the system identification and the acoustic echo cancellation with double-talk. [ABSTRACT FROM AUTHOR]

Proposed is a novel variable step size normalized subband adaptive filter algorithm, which assigns an individual step size for each subband by minimizing the mean square of the noise-free a posterior subband error. Furthermore, a noniterative shrinkage method is used to recover the noise-free priori subband error from the noisy subband error signal. Simulation results using the colored input signals have demonstrated that the proposed algorithm not only has better tracking capability than the existing subband adaptive filter algorithms, but also exhibits lower steady-state error. [ABSTRACT FROM AUTHOR]

The filtered-x least mean lp -norm (FxLMP) algorithm is proven to be useful for nonlinear active noise control (NANC) systems. However, its performance deteriorates when the impulsive noises are presented in NANC systems. To surmount this shortcoming, a new nonlinear adaptive algorithm based on Volterra expansion model (VFxlogLMP) is developed in this paper, which is derived by minimizing the lp -norm of logarithmic cost. It is found that the FxLMP and VFxlogLMP require to select an appropriate value of p according to the prior information on noise characteristics, which prohibit their practical applications. Based on VFxlogLMP algorithm, we proposed a continuous lp -norm algorithm with logarithmic cost (VFxlogCLMP), which does not need the parameter selection and thresholds estimation. Benefiting from the various error norms for 1≤ p ≤2, it remains the robustness of VFxlogLMP. Moreover, the convergence behavior of VFxlogCLMP for moving average secondary paths and stochastic input signals is performed. Compared to the existing algorithms, two versions of the proposed algorithms have much better convergence and stability in impulsive noise environments. [ABSTRACT FROM AUTHOR]

We propose an affine projection M-estimate subband adaptive filter (APM-SAF), which is characterized by its robustness against impulsive noise. Instead of the conventional mean square error (MSE) function, the proposed APM-SAF employs a robust M-estimator-based cost function which is able to suppress the effect of impulsive noise on the filter weight update. Furthermore, to improve the performance of the APM-SAF for sparse impulse response, an improved proportionate APM-SAF (IP-APM-SAF) is proposed by exploiting the sparsity of the impulse response. Simulations in an acoustic echo cancellation (AEC) context show that the APM-SAF performs better than the conventional NSAFs and SSAFs. The improvement in convergence rate is also demonstrated for the IP-APM-SAF. [ABSTRACT FROM AUTHOR]

Recently, the sign subband adaptive filter (SSAF) algorithm has obtained great attention, due to its robustness against impulsive noises and decorrelating property for correlated input signals. However, the performance of the algorithm in the steady-state is not analyzed. In this paper, we study the steady-state mean-square-deviation (MSD) behavior of the SSAF algorithm by using energy conservation relation, Price׳s theorem and some reasonable assumptions. Simulation results in different system identification scenarios (including the input signals, tap lengths, impulsive noises, number of subbands, and step sizes) are provided to support our theoretical analysis. [ABSTRACT FROM AUTHOR]

Proposed is a new subband adaptive filter (SAF) algorithm by minimizing Huber’s cost function that is robust to impulsive noises. Generally, this algorithm works in the mode of the normalized SAF (NSAF) algorithm, while it behaves like the sign SAF (SSAF) algorithm only when the impulsive noises appear. To further improve the robustness of this algorithm against impulsive noises, the subband cutoff parameters are updated in a recursive way. Moreover, the proposed algorithm can be interpreted as a variable step size NSAF algorithm, thus it exhibits faster convergence rate and lower steady-state error than the NSAF. Simulation results, using different colored input signals in both impulsive and free-impulsive noise environments, show that the proposed algorithm works better than some existing algorithms. [ABSTRACT FROM AUTHOR]

In order to reduce the steady-state misalignment of real-coefficient proportionate affine projection sign algorithm (RP-APSA) for sparse impulse responses, a memory RP-APSA is proposed in this paper by exploiting the historical values of proportionate factors, called MP-APSA. Further, to improve the robustness of MP-APSA and the recently proposed memory-improved RP-APSA (MIP-APSA) for impulse responses with mutative sparseness, two sparseness-controlled algorithms (SC-MP-APSA and SC-MIP-APSA) are developed by estimating the sparseness of the impulse response at each iteration. Simulation results in the context of acoustic echo cancellation show that the proposed algorithms retain good robustness against impulsive interferences. More importantly, the proposed sparseness-controlled algorithms can not only obtain low steady-state misalignment in both the sparse and dispersive situations, but also adapt to the variations in the sparseness of the impulse response. [ABSTRACT FROM AUTHOR]

To improve the performance and reduce the computational complexity, a novel sign adaptation scheme for convex combination of adaptive filters is proposed. By using a sign adaptation scheme, the proposed scheme slightly reduces the computational complexity of the basic combination of mixing parameter, and improves the robustness of mixing parameter. Moreover, to obtain fast convergence property during the period of convergence transition in different environments, the instantaneous transfer scheme is applied to the proposed algorithm. The instantaneous transfer scheme has lower computational complexity than that of the basic combination of least mean square (basic-CLMS) algorithms. Simulation results in the context of system identification under time-invariant and time-variant systems demonstrate that the proposed algorithm based on the instantaneous transfer scheme has lower computational complexity and faster convergence rate than that of the basic-CLMS algorithm during period of convergence transitions. [ABSTRACT FROM AUTHOR]

• We first propose the constrained recursive least M-estimate (CRLM) algorithm by introducing the MHF into the cost function of CRLS, which is robust to impulsive noise. Further, an enhanced version of the CRLM algorithm, namely RCRLM algorithm was also derived to eliminate the round-off error caused by long input data sequence. • The computational complexity analysis of the proposed algorithms is given and compared with some other constrained algorithms. • Based on some simplified assumptions, we analyze the mean and mean square stability and theoretical transient and steady state behaviors under contaminated-Gaussian (CG) noise model. • Finally, the validity of theoretical analysis results is proved by linear system identification simulation. Further, the application of beamforming also shows the superior performance of the proposed RCRLM algorithm. Recently, the constrained adaptive filtering algorithms with strong robustness to non-Gaussian noise have been widely studied. Among them, the robust constrained least mean M-estimate (CLMM) algorithm has significant performance in the background of impulse noise base on the well anti-impulse noise characteristic of the M-estimate function. However, there is an irreconcilable contradiction between the steady-state error and the convergence rate of CLMM. To solve this contradiction, this paper introduces the modified Huber function (MHF) into the constrained recursive least squares (CRLS) algorithm and develops the constrained recursive least M-estimate (CRLM) algorithm, which fully combines the superior convergence performance of CRLS and the anti-impulse noise characteristic of the MHF. We furthermore propose an enhanced version of the CRLM, namely robust CRLM (RCRLM), which is robust to round-off error. Then, We analyze the mean and mean square stability and transient, steady-state NMSD of the proposed RCRLM algorithm under some simplified assumptions. Simulation results in various non-Gaussian noise scenarios show that the proposed RCRLM is superior to some existing constraint algorithms in terms of convergence speed, steady-state error and tracking ability, and the theoretical analysis results are also verified. [ABSTRACT FROM AUTHOR]

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]

Highlights: [•] A scheme of multichannel nonlinear ANC systems based on the adaptive bilinear filter is designed. [•] A simplified bilinear leaky filtered-x LMS algorithm for multichannel nonlinear ANC systems is derived. [•] The computational complexity of the proposed algorithm for multichannel nonlinear ANC systems is analyzed. [ABSTRACT FROM AUTHOR]

Highlights: [•] Exposure to lanthanides impaired renal function in mice. [•] Exposure to lanthanides induced cell necrosis in kidney. [•] Exposure to lanthanides promoted ROS accumulation in kidney. [•] Exposure to lanthanides caused the reduction of antioxidant capacity in kidney. [•] The order of kidney damages was Ce exposure>Nd exposure>La exposure. [Copyright &y& Elsevier]

To solve the problem that the performance of maximum total correntropy (MTC) algorithm is limited by kernel width, a more flexible algorithm called maximum mixture total correntropy (MMTC) is proposed in this brief, which is derived by combining the mixture correntropy (MC) and total least square (TLS) method. Then, the local mean and mean square performance of the MMTC algorithm are analyzed in detail under some simplifying assumptions, the stable step size range and theoretical steady-state MSD expression are also given. Numerical simulations exhibit that the MMTC performs better than MTC for the parameter estimation of the error-in-variable (EIV) model in the background of impulse noise, the validity of the theoretical analysis is also verified. [ABSTRACT FROM AUTHOR]

• In this paper, we analyze the transient performance of the geometric algebra based least mean M-estimate (GA-LMM) filtering algorithm in detail under some simplifying assumptions and give the step size range that ensure the mean square stability of the GA-LMM. Further, to eliminate the constraint of the constant step size on the performance of the GA-LMM, a novel variable step-size algorithm called VSS-GA-LMM is designed and the optimal step size is obtained by maximizing the difference of mean square deviation (MSD) between successive iterations, which effectively balances the contradiction between convergence rate and steady-state error. • The validity of the transient performance analysis about GA-LMM and the advantages of the GA-LMM and VSS-GA-LMM algorithms over other existing GA based algorithms are confirmed through numerical simulations. In this paper, the transient performance of the geometric algebra based least mean M-estimate (GA-LMM) filtering algorithm is analyzed in detail under some simplifying assumptions. Further, the variable step-size variant VSS-GA-LMM is designed to eliminate the constraint of the constant step size on the performance of the GA-LMM and the optimal step size is obtained by maximizing the difference of mean square deviation (MSD) between successive iterations, which effectively balances the contradiction between convergence rate and steady-state error. Finally, numerical simulations are presented to verify the validity of the theoretical analysis of the GA-LMM and the advantages of the GA-LMM and VSS-GA-LMM algorithms. [ABSTRACT FROM AUTHOR]

Cerium is widely used in many aspects of modern society, including agriculture, industry and medicine. It has been demonstrated to enter the ecological environment, is then transferred to humans through food chains, and causes toxic actions in several organs including the brain of animals. However, the neurotoxic molecular mechanisms are not clearly understood. In this study, mice were exposed to 0.5, 1, and 2 mg/kg BW cerium chloride (CeCl3) for 90 consecutive days, and their learning and memory ability as well as hippocampal gene expression profile were investigated. Our findings suggested that exposure to CeCl3 led to hippocampal lesions, apoptosis, oxidative stress and impairment of spatial recognition memory. Furthermore, microarray data showed marked alterations in the expression of 154 genes involved in learning and memory, immunity and inflammation, signal transduction, apoptosis and response to stress in the 2 mg/kg CeCl3 exposed hippocampi. Specifically, the significant up-regulation of Axud1, Cdc37, and Ube2v1 caused severe apoptosis, and great suppression of Adcy8, Fos, and Slc5a7 expression led to impairment of mouse cognitive ability. Therefore, Axud1, Cdc37, Ube2v1, Adcy8, Fos, and Slc5a7 may be potential biomarkers of hippocampal toxicity caused by CeCl3 exposure. [ABSTRACT FROM AUTHOR]

Abstract: To improve performance of nonlinear adaptive filter based on radius basis function (RBF) networks, a generalized combination scheme is proposed for nonlinear dynamic system identification in this paper. The nonlinear filter proposed is constructed by the convex combination of multiple RBF networks (MCRBF). Its adaptive algorithm with different step sizes is derived by the gradient descent rule, and can overcome the contradiction between convergence speed and precision of the stochastic gradient (SG) algorithm for RBF networks, which is imposed by the selection of a fixed value for the adaption step. Computer simulations demonstrate that the performance of the nonlinear filter proposed is superior to the RBF for nonlinear dynamic system identification in terms of convergence speed, steady state error and tracking capability. [Copyright &y& Elsevier]

Abstract: This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter. [Copyright &y& Elsevier]

Many network processing applications require wirespeed access to large data structures or a large amount of packet and flow-level data. Therefore, it is essential for the memory system of a router to be able to support both read and write accesses to such data at link speeds. As link speeds continue to increase, router designers are constantly grappling with the unfortunate trade-offs between the speed and cost of SRAM and DRAM. The capacity of SRAMs is woefully inadequate in many cases and it proves too costly to store large data structures entirely in SRAM, while DRAM is viewed as too slow for providing wirespeed updates at such high speed. In this paper, we analyze a robust pipelined memory architecture that can emulate an ideal SRAM by guaranteeing with very high probability that the output sequence produced by the pipelined memory architecture is the same as the one produced by an ideal SRAM under the same sequence of memory read and write operations, except time shifted by a fixed pipeline delay of \Delta. Given a fixed pipeline delay abstraction, no interrupt mechanism is required to indicate when read data are ready or a write operation has completed, which greatly simplifies the use of the proposed solution. The design is based on the interleaving of DRAM banks together with the use of a reservation table that serves in part as a data cache. In contrast to prior interleaved memory solutions, our design is robust under all memory access patterns, including adversarial ones, which we demonstrate through a rigorous worst case theoretical analysis using a combination of convex ordering and large deviation theory. [ABSTRACT FROM AUTHOR]

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models. [ABSTRACT FROM AUTHOR]

Abstract: To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. [ABSTRACT FROM AUTHOR]

Abstract: In actual nonlinear active noise control (NANC) systems, there often exist nonlinear distortions in such cases: the primary path may be nonlinear, the reference noise may exhibit nonlinear distortion, and the secondary path may have nonminimum-phase. To solve the problems of nonlinear distortions, two novel feedback adaptive filters based on the functional link neural network (FLNN) for NANC systems with low computational complexity are proposed in this paper, which are a feedback functional link neural network (FFLNN) and a reduced feedback functional link neural network (RFFLNN), respectively. To train the proposed nonlinear filters for NANC systems, a reduced complexity filtered-s least mean square (FSLMS) algorithm using filter bank approach is developed. The analysis of computational complexity shows that the RFFLNN adaptive filter involves less computation as compared to FFLNN and FLNN adaptive filters. Moreover, it is demonstrated through computer simulations for nonlinear noise processes that the RFFLNN adaptive filter outperforms FLNN and FFLNN in term of convergence speed and steady-state error. Furthermore, it is more effective in reducing nonlinear effects in NANC systems than other filters. [Copyright &y& Elsevier]

An incremental least‐mean M‐estimate algorithm is proposed for distributed network, which minimises a robust M‐estimator‐based cost function against impulsive noise via the gradient descent method. Moreover, based on the same frame for implementation, an incremental distributed least‐mean p‐power algorithm is presented for combating impulsive noise. Simulations verify the superiority of the proposed algorithms in the presence of impulsive noise relative to some existing incremental distributed algorithms. [ABSTRACT FROM AUTHOR]

A new class of bias‐compensated affine‐projection‐like (APL) algorithms is proposed, in which a bias‐compensation vector is derived to eliminate the bias caused by the noisy input. In addition, a new estimation method for the input noise variance is proposed which does not require the input–output noise variance ratio in advance. Simulations in a system identification context show that the proposed algorithms achieve significant improvements in steady‐state misalignment as compared with the conventional APL algorithms. [ABSTRACT FROM AUTHOR]

Proposed is an L0 norm constraint set‐membership affine projection algorithm with coefficient vector reuse, which is derived by minimising a differentiable cost function that utilises the L0 norm of the updated weight vector as well as the sum of the squared Euclidean norms of the differences between the updated weight vector and past weight vectors. In addition, a power estimate method is introduced to eliminate the effect of the fluctuation of the output error on the step size. Simulations on sparse system identifications demonstrate that the proposed algorithm achieves a lower steady‐state misalignment than the existing algorithms in a high background noise environment. [ABSTRACT FROM AUTHOR]