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Purpose: The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations. Design/methodology/approach: This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions. Findings: FEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS's superior performance against existing benchmark models. Originality/value: The originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network's expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main). [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive event-triggered fault detection filter to warn of faults happening to unmanned surface vehicles in a network. First, the unmanned surface vehicle is modeled as a Markovian jump system which makes allowances for the influences of waves or other disturbances. Then, a fault detection filter is developed to produce crucial residual signals, despite disturbances and denial-of-service attacks. Inputs of the designed filter originate from a resilient adaptive event-triggered mechanism, of which the threshold can be adjusted for saving valuable network resources and mitigating adverse influences of denial-of-service attacks. By using the residual signals, detection evaluation functions are established and a detection logic algorithm is devised. Under this framework, the co-design of both the fault detection filter and the adaptive event-triggered scheme is given. Finally, the proposed method is proved to be applicable by simulating a real unmanned surface vehicle model. [ABSTRACT FROM AUTHOR]

This paper focuses on the output feedback tracking control of a high-order nonlinear system with denial-of-service (DoS) attacks and exogenous disturbances. A radial basis function neural network (RBFNN)-based state observer is employed to estimate the system states. A new nonlinear filter with adaptive regulation is developed to eliminate the impacts of perturbed factors such as RBFNN approximation errors and disturbances. Constituting with filtering operation and backstepping control technique, a RBFNN-based security controller is constructed to suppress the influences of perturbations and DoS attacks based on the observation signals. The uniformly ultimately bounded output tracking result is established by using the security control signals via Lyapunov criterion in the cases of DoS attacks, nonlinearities, and disturbances. Comparative results are provided to validate the efficiency of the developed RBFNN-based observation and security control strategies of a nonlinear system. [ABSTRACT FROM AUTHOR]

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in distributed parameter estimation and target tracking of multitask network. However, its performance degrades under colored input signals or impulsive noises. To overcome these two drawbacks, this paper introduces the subband adaptive filter (SAF) into a multitask network for the first time, and a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed, which solves the global network optimization problem based on the modified Huber (MH) function in a distributed manner, achieves robustness to impulsive noise, and significantly improves the convergence performance of the MD-LMS algorithm. Compared with existing robust multitask diffusion affine projection algorithms (MD-APAs), the computational complexity of the proposed MD-NMSAF algorithm is greatly reduced. In addition, we analyze the mean and mean-square stability conditions of MD-NMSAF, provide theoretical models characterizing the network mean square deviation (MSD) behaviors of transient and steady-state, and further verify their correctness by computer simulations. Simulation results under different network topologies, input signals, filter lengths and impulsive noise types fully demonstrate the performance advantages of the proposed MD-NMSAF algorithm over its competitors in terms of steady-state estimation accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

A family of hypercomplex algebras in four dimensions (4D) is proposed to devise adaptive filters. Such a family, called β -quaternions, has multiplication rules for the complex units that depend on a parameter β , and this family contains, as particular cases, both standard Hamilton quaternions and split quaternions. In this framework, two notions of properness for random vectors are introduced and their implications on the statistical processing involved are analyzed. Then, statistical tests to check properness in practice and a method to select the best algebra where the properness conditions could hold are provided. Also, proper adaptive filters are suggested and row and column updating problems are studied. The main advantage of the techniques proposed compared with the standard ones is that a notable reduction in the computational burden is achieved. Finally, simulation examples validate the proper adaptive filters and demonstrate that our scheme performs better than the traditional quaternion domain. [ABSTRACT FROM AUTHOR]

Recently, the augmented complex-valued normalized subband adaptive filtering (ACNSAF) algorithm has been proposed to process colored non-circular signals. However, its performance will deteriorate severely under impulsive noise interference. To overcome this issue, a robust augmented complex-valued normalized M-estimate subband adaptive filtering (ACNMSAF) algorithm is proposed, which is obtained by modifying the subband constraints of the ACNSAF algorithm using the complex-valued modified Huber (MH) function and is derived based on CR calculus and Lagrange multipliers. In order to improve both the convergence speed and steady-state accuracy of the fixed step size ACNMSAF algorithm, a variable step size (VSS) strategy based on the minimum mean squared deviation (MSD) criterion is devised, which allocates individual adaptive step size to each subband, fully exploiting the structural advantages of SAF and significantly improving the convergence performance of the ACNMSAF algorithm as well as its tracking capability in non-stationary environment. Then, the stability, transient and steady-state MSD performance of the ACNMSAF algorithm in the presence of colored non-circular inputs and impulsive noise are analyzed, and the stability conditions, transient and steady-state MSD formulas are also derived. Computer simulations in impulsive noise environments verify the accuracy of theoretical analysis results and the effectiveness of the proposed algorithms compared to other existing complex-valued adaptive algorithms. [ABSTRACT FROM AUTHOR]

As a robust online learning approach, adaptive filtering based on correntropy has attracted more attentions. Recently, researchers have made efforts to develop complex correntropy to deal with adaptive filtering issues in complex domain. However, most of the literature on complex correntropy lack details regarding the processing of noncircular signals. Up to now, the maximum improper complex correntropy criterion (MICCC) algorithm for noncircular and widely-linear (WL) adaptive filtering utilizes the gradient descent method to find the maximum of the objective function. However, the MICCC algorithm fails to guarantee both low steady-state weight error power and fast convergence. To overcome this defect, this paper proposes a class of adaptive filtering algorithms which are based on fixed-point instead of gradient descent. It is worth noting that we manage to avoid the massive calculation of matrix inversion in traditional fixed-point based methods by utilizing the lemma of matrix inversion properly and further provide two alternative recursive algorithms. Finally, the local convergence analysis of mean and mean square is performed, and the simulated results offer strong evidences for the superior performance of the proposed algorithms over widely-linear complex least mean square (WL-CLMS), widely-linear recursive least square (WL-RLS) and MICCC. [ABSTRACT FROM AUTHOR]

The performance of the current state estimation will degrade in the existence of slow-varying noise statistics. To solve the aforementioned issues, an improved strong tracking maximum correntropy criterion variational-Bayesian adaptive Kalman filter is presented in this paper. First of all, the inverse-Wishart distribution, as the conjugate-prior, is adopted to model the unknown and time-varying measurement and process noise covariances, then the noise covariances and system state are estimated via the variational Bayesian method. Secondly, the multiple fading-factors are obtained and evaluated to modify the prediction error covariance matrix to address the problems associated with inaccurate error estimation. Finally, the maximum correntropy criterion is employed to correct the filtering gain, which improves the filtering performance of the proposed algorithm. Simulation results show that the proposed filter exhibits better accuracy and convergence performance compared to other existing algorithms. [ABSTRACT FROM AUTHOR]

To ensure better performance and simultaneously save resources, an event-triggered adaptive command filtered dynamic surface control (ACFDSC) method for uncertain stochastic nonstrict-feedback nonlinear systems with dynamic output constraints and prescribed performance is designed in this article. Firstly, with the help of reduced-order K-filters, linearly parameterized neural networks and specific coordinate transformation technique, the unmeasurable states, nonlinearities, two types of unmodeled dynamics and output constraints are dealt with respectively. Then, an event-triggered ACFDSC strategy is proposed to ensure that the tracking error reaches a specific bound within a finite time. By introducing the compensated signal into the complete Lyapunov function, and with the assistance of the compact set defined in the stability analysis, all signals are strictly demonstrated to be semi-globally uniformly ultimately bounded. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Graph neural network is a breakthrough in applying deep learning to non-Euclidean space. It is widely used for tasks such as social network analysis, molecular function inference, drug repositioning and protein modeling, achieving outstanding performance on relational models. Despite the great success of graph neural networks, most of them cannot be generalized to various scenarios. This is because graph information needs variation for different tasks and fixed models limit the flexibility of feature extraction. To address this challenge, we design a graph filter that can be adaptively adjusted according to graph tasks. This filter combines the multi-view strategy with the learnable quadratic frequency response function, using the crests of the quadratic functions to adaptively emphasize the required information. We further design the graph convolutional network model base on this adaptive filter, named AF-GCN. Extensive experiments are performed with 13 SOTA models on 12 different real-world datasets, including homogeneous and heterogeneous datasets in the node classification task, biological datasets, and social network datasets in the graph classification task. AF-GCN achieves state-of-the-art results in various scenarios. In addition, AF-GCN has high interpretability in the graph spatial domain. The development process from Graph Convolutional Networks (GCN) to AF-GCN has historical similarities with the development of Convolution Neural Networks (CNN). [ABSTRACT FROM AUTHOR]

In this paper, a high-order command filtered adaptive backstepping (HOCFAB)-based approach is proposed in order to track a given reference signal for the second- and high-order strict-feedback systems (SFSs) with parametric uncertainties, where both their subsystems hold a common full-actuation structure, namely, high-order fully actuated (HOFA) SFSs. Unlike the prevailing traditional first-order state-space backstepping approach which suffers from the problem of "explosion of terms", the proposed HOCFAB approach circumvents the complexity arising owing to differentiating the virtual controllers repeatedly, and does not need to convert the high-order systems into first-order forms which is easier to carry out and demands fewer steps. Meanwhile, an error-compensating mechanism is constructed to reduce filtering errors. A critical analysis is theoretically proven which indicates that in both cases the entire system states are uniformly ultimately bounded under the proposed high-order controller, and the tracking error could be made arbitrarily small with predesigned parameters. Finally, the effectiveness of the proposed scheme is verified by a benchmark application in the robotic manipulator. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive event-triggered filter of positive Markovian jump systems based on disturbance observer. A new adaptive event-triggering mechanism is constructed for the systems. A positive disturbance observer is designed for the systems to estimate the disturbance. A distributed output model of each subsystem of positive Markovian jump systems is introduced. Then, an adaptive event-triggering distributed filter is designed by employing stochastic copositive Lyapunov functions. All presented conditions are solvable in terms of linear programming. Under the designed disturbance observer and the distributed filter, the corresponding error system is stochastically stable. The filter design approach is also developed for discrete-time positive Markovian jump systems. The contribution of the paper lies in that: (i) A new adaptive event-triggering mechanism is established for positive systems, (ii) A positive disturbance observer is designed for the disturbance of positive Markovian jump systems, and (iii) The designed distributed filter can guarantee the stochastic stability of the error while existing filters in literature only achieve the stochastic gain stability of the error. Finally, two examples are given to illustrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

Popular network pruning algorithms reduce redundant information by optimizing hand-crafted models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce adaptive exemplar filters to simplify the algorithm design, resulting in an automatic and efficient pruning approach called EPruner. Inspired by the face recognition community, we use a message-passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters. EPruner breaks the dependence on the training data in determining the “important” filters and allows the CPU implementation in seconds, an order of magnitude faster than GPU-based SOTAs. Moreover, we show that the weights of exemplars provide a better initialization for the fine-tuning. On VGGNet-16, EPruner achieves a 76.34%-FLOPs reduction by removing 88.80% parameters, with 0.06% accuracy improvement on CIFAR-10. In ResNet-152, EPruner achieves a 65.12%-FLOPs reduction by removing 64.18% parameters, with only 0.71% top-5 accuracy loss on ILSVRC-2012. Our code is available at https://github.com/lmbxmu/EPruner. [ABSTRACT FROM AUTHOR]

This paper addresses the design of neural network observer and adaptive finite-time tracking controller for uncertain nonlinear with event-triggered inputs and unknown dead-zone constraints. To shorten the convergence time and reduce the computational burden, the finite-time command filter backstepping (CFB) technique is modified by improving the error compensation term, and a novel adaptive output feedback event triggering mechanism is developed in this way. It is proven that the newly presented control strategy achieves the goal of finite-time convergence and effectively saves network bandwidth. Meanwhile, all of the closed-loop signals are bounded, and the tracking performance is guaranteed based on the finite-time CFB method avoiding the problem of complexity explosion. Finally, a practical example is included to verify the validity of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a command filter based dynamic surface control (DSC) is developed for stochastic nonlinear systems with input delay, stochastic unmodeled dynamics and full state constraints. An error compensation system is designed to constrain the filtering error caused by the first-order filter in the traditional dynamic surface design. On this basis, the stability proof of DSC for stochastic nonlinear systems based on command filter is proposed. The definition of state constraints in probability is presented, and the problem of stochastic full state constraints is solved by constructing a group of coordinate transformations with nonlinear mappings. The Pade approximation is adopted to deal with input delay. The stochastic unmodeled dynamics is considered, which is processed by utilizing the property of stochastic input-to-state stability (SISS) and changing supply function. All the signals of the system are proved to be semi-globally uniformly ultimately bounded (SGUUB) in probability, and the full state constraints are not violated. The two simulation examples also verify the effectiveness of the proposed adaptive DSC scheme. [ABSTRACT FROM AUTHOR]

• To overcome the trade-off between fast convergence and low steady-state misalignment under the fixed step-size, we devised a family of variable step-size strategy S-IWF-SSAF algorithms based on the transient model of algorithms by minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. • In order to further enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. And to guarantee the convergence, the stable step-size range in the mean and mean square sense is concluded. In addition, the computational requirements are exhibited as well. • Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to state-of-the-art algorithms within various system inputs under impulsive interference environments. Recently, the sparsity-aware sign subband adaptive filter algorithm with individual-weighting-factors (S-IWF-SSAF) was devised. To accomplish performance enhancement, the variable parameter S-IWF-SSAF (VP-S-IWF-SSAF) algorithm was developed through optimizing the step-size and penalty factor, respectively. Different from the optimization scheme, we devise a family of variable step-size strategy S-IWF-SSAF (VSS-S-IWF-SSAF) algorithms based on the transient model of algorithms via minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. And in order to enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. It is worth mentioning that the presented algorithms could acquire lower computational requirements and exhibit higher steady-state estimation accuracy obviously and acceptable tracking characteristic in comparison to the VP-S-IWF-SSAF algorithm. In addition, the stable step-size range in the mean and mean square sense and steady-state performance are concluded. And the computational requirements are exhibited as well. Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to other related algorithms within various system inputs under impulsive interference environments. [ABSTRACT FROM AUTHOR]

Classical mean-square error (MSE)-based adaptive filtering algorithms are useful for noise-free inputs. However, if the input signal of the adaptive filter with such an adaptive filtering algorithm is corrupted by noise, it will suffer from serious degradation of steady-state performance. To address the problem, a new adaptive filtering algorithm is proposed in this work. This algorithm not only uses the total method to compensate the bias caused by noisy input but also minimizes the cost function based on logistic distance metric (LDM) to achieve robustness against impulsive noise. Compared with the existing algorithms, the proposed least total logistic distance metric (LTLDM) algorithm has less misalignment when the input signal is disturbed by noise and has good robustness to impulsive noise. This work also tackles the crucial trade-off between convergence rate and misalignment by developing a variable step-size for LTLDM. In addition, to give deep insight into the stochastic behavior of the proposed LTLDM, its mean-square deviation is analyzed at steady state. The advantage of LTLDM and the accuracy of theoretical expressions are verified by extensive simulations. [ABSTRACT FROM AUTHOR]

A patent application has been submitted for a filtering method and filtering control apparatus for load cells. Load cells are used in weighing systems, but they can be affected by various factors that cause weighing signal errors. The proposed method aims to eliminate these errors by using multiple filtering units with different settling times to filter the weighing data. The method determines the target filtering unit based on the weighing requirements, such as precision and speed. The filtering control apparatus, which includes a memory and a controller, implements the filtering method. This invention expands the application scope of filter adaptive technology and improves its effectiveness in weighing processes. [Extracted from the article]

Hammerstein structure is commonly used for describing nonlinear dynamic characteristics, and its identification is a basic premise of nonlinear system analysis and control. This paper investigates online identification methods for a class of Hammerstein nonlinear systems, which consists of a nonlinear memoryless element followed by a linear output-error subsystem. The unmeasurable noise-free output of the linear subsystem makes the model parameters cannot be directly estimated by traditional identification methods. To address this difficulty, by using a series of weighted particles to adaptively approximate the posterior probability density function of the unmeasurable noise-free output, this paper proposes a particle filter-based stochastic gradient algorithm. Moreover, to enhance the data utilization and estimation accuracy, a particle filter-based multi-innovation stochastic gradient algorithm is developed through the innovation expansion technique. The simulation results demonstrate that compared with the existing benchmark algorithms, the proposed algorithms need a little more computational time due to the introduction of the adaptive particle filter, but they have the improved identification accuracies. [ABSTRACT FROM AUTHOR]

This article devises a spline adaptive filter (SAF) algorithm with robustness for nonlinear adaptive noise cancellation (NANC), called the SAF-SDM, which is built on a distance-based metric with a sigmoid kernel. The presented SAF-SDM delivers insignificant coefficient updates when the system is subjected to remarkable outliers, which leads to low steady-state errors. Additionally, we thoroughly analyze the mean behaviour and mean-square performance of the SAF-SDM at the theoretical level and validate the analysis results using nonlinear system identification (NSI) simulations. To fulfill the higher computational efficiency when a longer linear filter order is selected in the SAF, an improved SAF-SDM is developed through a frequency domain (FD) filtering program, called the FDSAF-SDM. Then, we provide an analysis of the calculation complexity of the two presented algorithms and compare them with those of several similar algorithms. Finally, simulation and experiment results demonstrate the excellence of the presented algorithms in NSI and NANC. [ABSTRACT FROM AUTHOR]

In system identification, if the filter input and the desired signal are both interfered by noise, traditional adaptive filtering algorithms will not work effectively. The recently presented bias-compensated normalized maximum correntropy criterion (BC-NMCC) algorithm, which was developed based on the information theory learning (ITL) and Taylor expansion, can deal with this case. However, it needs to use measurement noise samples to update the adaptive filter weight vector, whose values are usually unavailable. To solve this problem, we propose a direct integration bias-compensated maximum correntropy criterion (DI-BC-MCC) algorithm under an unbiasedness criterion (UC). Specifically, we utilize a direct integral approach to calculate the expected values instead of using Taylor expansion. With this idea, the update equation of DI-BC-MCC does not involve measurement noise samples, unlike BC-NMCC. The derived compensation term can reduce the impact of input noise significantly, and DI-BC-MCC can perform well in impulsive noise environments. In addition, the steady-state performance of DI-BC-MCC is analyzed under some frequently used assumptions. Finally, simulations are provided to demonstrate the good performance of DI-BC-MCC and to verify the accuracy of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSIC) is essential in remote sensing image analysis. Applying a graph neural network (GNN) to hyperspectral image (HSI) classification has attracted increasing attention. However, the available GNNs for HSIC only adopt a kind of graph filter and an aggregator, which cannot well deal with the problems of land cover discrimination, noise impaction, and spatial feature learning. To overcome these problems, a graph convolution with adaptive filters and aggregator fusion (AF2GNN) is developed for HSIC. To reduce the number of graph nodes, a superpixel segment algorithm is employed to refine the local spatial features of the HSI. A two-layer 1D CNN is proposed to transform the spectral features of superpixels. In addition, a linear function is designed to combine the different graph filters, with which the graph filter can be adaptively determined by training different weight matrices. Moreover, degree-scalers are defined to combine the multiple filters and present the graph structure. Finally, the AF2GNN is proposed to realize the adaptive filters and aggregator fusion mechanism within a single network. In the proposed network, a softMax function is utilized for graph feature interpretation and pixel-label prediction. Compared with state-of-the-art methods, the proposed method achieves superior experimental results. [ABSTRACT FROM AUTHOR]

• A novel fast FTC scheme based on command filter and neural network is proposed by using backstepping control for the HRV with complex uncertainties. • The command filter and adaptive neural network are combined to reconstruct system nonlinearities and deal with complex uncertainties in the HRV.. • The novel fast FTC scheme solves the complexity explosion problem in the standard backstepping control. In this paper, a novel fast attitude adaptive fault-tolerant control (FTC) scheme based on adaptive neural network and command filter is presented for the hypersonic reentry vehicles (HRV) with complex uncertainties which contain parameter uncertainties, un-modeled dynamics, actuator faults, and external disturbances. To improve the performance of closed-loop FTC, command filter and neural network are introduced to reconstruct system nonlinearities that are related to complex uncertainties. Compared with the FTC scheme with only neural network, the FTC scheme with command filter and neural network has fewer controller design parameters so that the computational complexity is decreased and the control efficiency is improved, which is of great significance for HRV. Then, the adaptive backstepping fault-tolerant controller based on command filter and neural network is designed, which can solve the complexity explosion problem in the standard backstepping control and the small uncertainty problem in the backstepping control only containing command filter. Moreover, to improve the approximation accuracy of the neural network-based universal approximator, an adaptive update law of neural network weights is designed by using the convex optimization technique. It is proved that the presented FTC scheme can ensure that the closed-loop control system is stable and the tracking errors are convergent. Finally, simulation results are carried out to verify the superiority and effectiveness of the presented FTC scheme. [ABSTRACT FROM AUTHOR]

This article is devoted to the adaptive asymptotic tracking for a class of uncertain nonlinear systems. The presence of unknown time-varying parameters and uncertain disturbances makes the systems in question essentially different from those in the related works. By skillfully combining adaptive technique and command filter-based backstepping, a novel command filter adaptive tracking controller is successfully designed to achieve asymptotic tracking. The typical feature of the proposed controller lies in the introduction of a smooth function with positive integrable time-varying function, which makes the controller powerful enough to compensate the unknown time-varying parameters and uncertain disturbances. Remarkably, a novel Lyapunov function by incorporating the lower bounds of control gains is used to prove the stability of the closed-loop system. Compared with some existing command filter-based backstepping, the conditions on the virtual control coefficients and disturbances are relaxed. Finally, the effectiveness of the proposed method is shown by a simulation example. [ABSTRACT FROM AUTHOR]

This paper considers a bearings-only tracking problem with unknown measurement noise statistics. It is assumed that the measurement noise follows a Gaussian probability density function where the mean and the covariance of the noise are unknown. Here, an adaptive nonlinear filtering technique is proposed, where the joint distribution of the measurement noise mean and its covariance are considered to follow a normal inverse Wishart (NIW) distribution. Using the variational Bayesian (VB) approximation, joint distribution of the target state, the measurement noise mean and covariance is factorized as the product of their individual probability density function (pdf). Minimizing the Kullback-Leibler divergence (KLD) between the factorized and true joint pdfs, probability distributions of the noise mean, covariance and the target states are evaluated. The estimation of states with the proposed VB based method is compared with the maximum a posteriori (MAP) and the maximum likelihood estimation (MLE) based adaptive filtering. Deterministic sigma points are used to realize the filtering algorithms. The proposed adaptive filter with VB approximation is found to be more accurate compared to their corresponding MAP-MLE based counterparts. [ABSTRACT FROM AUTHOR]

Linear filtering methods are well-known and have been successfully applied to system identification and equalization problems. However, when high-dimensional systems are modeled, these methods often perform unsatisfactorily due to their slow convergence and to the high number of parameters to estimate, which brings high computational and storage complexities. To cope with these difficulties, the assumption of a low-rank impulse response was recently exploited to derive computationally efficient adaptive tensor filtering methods. However, existing approaches either model the impulse response as a low-rank matrix or a rank-1 tensor. While the former choice can only bring a limited complexity reduction, the latter relies on a strong assumption that is too restrictive for many systems of interest. In this work, we propose an adaptive filtering approach that models the impulse response more generally as a low-rank tensor, with a rank possibly higher than one and order possibly higher than two. This approach is suitable for problems involving the identification of a high-dimensional system whose impulse response has a multilinear low-rank structure. It can overcome the curse of dimensionality by taking advantage of such a structure, which allows breaking a large system identification problem into several smaller ones. Simulation results compare the proposed algorithms with existing ones in terms of their performance, computational complexity, and memory demands. In particular, these results show that when the target impulse response has a tensor structure, our approach can achieve a faster convergence and lower steady-state error than standard LMS while having reduced memory complexity in comparison with the matrix-based approach. [ABSTRACT FROM AUTHOR]

The striking problem of acoustic feedback remains a persistent challenge in hearing aids (HAs), imposing limitations on attainable amplification and significantly deteriorating sound quality, often manifesting as disruptive howling artifacts. With the objective of feedback mitigation, the feedback path is estimated using an adaptive filter. The constrained least mean square (CLMS) technique is commonly employed in adaptive filtering applications where it is necessary to meet specific linear constraints. However, algorithm's robustness is compromised when impulsive or non-Gaussian noise interference occurs. In lieu of this, a novel algorithm known as the robust constrained cosine arctangent (CCAT) adaptive filtering is introduced to enhance the convergence behavior. This has been formulated with the inclusion of linear constraints to the suggested cosine function integrated with the arctangent technique. This algorithm aims to address the issue of feedback cancellation in hearing aids along a set of constraints. To achieve a minimal steady-state error and expedite the convergence of the method, a novel policy that adjusts the step factor has been suggested. CCAT-VSS is a modified version of CCAT that includes a variable step size (VSS). The simulation findings demonstrate that the suggested method exhibits superior performance for misalignment, added stable gain and certain voice quality assessments. • Acoustic feedback cancellation (AFC) in hearing aids in the presence of impulsive noise poses a serious problem. • A constrained cosine arctangent (CCAT) based cost function is suggested to improve the convergence characteristics for AFC. • A new variable step size (VSS) scheme is introduced with CCAT adaptive filtering (CCAT-VSS). • The convergence and MSD of the CCAT-VSS algorithm are evaluated for both Gaussian and non-Gaussian noises. [ABSTRACT FROM AUTHOR]

A recent report from the Beijing Institute of Technology discusses research on drones. The study focuses on the cooperative localization problem for dynamic UAV swarms in an anchor-limited environment. The researchers propose an adaptive Wasserstein filter with distance-constrained bare bones self-recovery particles (CBBP) to address this issue. The algorithm aims to suppress cumulative error from the inertial navigation system, update posterior weights of particles using the Wasserstein measure, and improve accuracy and stability through a fast bare bones particle self-recovery algorithm. The simulation results demonstrate the algorithm's robustness and competitive accuracy. For more information, readers can refer to the journal article "Multi-UAV Cooperative Localization Using Adaptive Wasserstein Filter with Distance-Constrained Bare Bones Self-Recovery Particles" in Drones. [Extracted from the article]

The document provides information about a clinical study, NCT06442488, that is currently being conducted in the United States. The study aims to evaluate the accuracy of a wearable respiratory monitoring system (RMS) in measuring various respiratory parameters in a noisy environment. The RMS consists of a miniature acoustic sensor and a flexible cradle that is attached to the neck. The study will compare the accuracy of the RMS with adaptive filtering and active noise cancellation turned on versus turned off. The study is recruiting participants and is expected to be completed by September 30, 2024. The study is led by Jeffrey Joseph from Thomas Jefferson University and does not plan to share data with other researchers. [Extracted from the article]

A recent study conducted by researchers at Jilin University in China has developed an adaptive Gaussian filtering algorithm for processing magnetic resonance sounding signals. This algorithm effectively reduces interference and noise in the signals, resulting in improved signal quality. The algorithm was tested and validated through simulations and field data processing, demonstrating its superior performance compared to other classical algorithms. The findings of this study have significant implications for the field of remote sensing and geophysical exploration. [Extracted from the article]

This paper focuses on the design of an adaptive fuzzy filter for discrete-time multi-area power systems, considering the presence of sojourn probabilities and deception attacks. To capture the dynamic behavior of multi-area power systems more accurately, an innovative switching law is introduced, incorporating nonhomogeneous sojourn probability information. The feedback sojourn probability information is determined by a deterministic switched signal, enhancing the system's stability and performance. With the aim of addressing the potential impact of measurement outlier, a robust filter is developed, incorporating a dynamic saturation constraint and fuzzy logic systems. The filter effectively mitigates the influence of outliers by adaptively adjusting the saturation level based on estimation errors. The stability analysis is carried out using Lyapunov theory and average dwell-time information, demonstrating the mean square exponentially ultimately bounded of the augmented system. To validate the proposed algorithm, a simulation example is presented, showcasing its effectiveness and practical applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Persymmetric structure has been utilized in space-time adaptive processing for heterogeneous environment, which leads to some detection methods based on persymmetric structure, such as persymmetric adaptive matched filter (PS-AMF). However, when the sample support is extremely limited, these methods still suffer the serious degradation in detection performance due to the large error in estimating covariance matrix. In this paper, an asymptotically optimal convex linear combination between transformed sample covariance matrix and identity matrix is considered herein to improve PS-AMF. The asymptotically optimal convex linear combination is conducive to diminishing the estimate error in estimating the transformed covariance matrix by weighting the transformed sample covariance matrix and identity matrix. Furthermore, the asymptotically optimal convex linear combination is improved by the reutilization of the convex linear combination, and the corresponding coefficients are derived. Then, the detection performance of the proposed method is approximately analyzed. At last, numerical simulations show that the proposed method performs well, compared with its counterparts, when the sample support is extremely limited. [ABSTRACT FROM AUTHOR]

Real-time seismic signals are intertwined with different types of noises during the generation, acquisition, and transmission process. The enhanced data with high resolution assists to interpret and analyze records more accurately. In this paper, we propose a mathematical approach based on recently developed geometrical mode decomposition (GMD) and adaptive self-guided filter (ASGF) to attenuate noise from two-dimensional seismic data. GMD is first applied to decompose the 2D seismic data into a number of band-limited intrinsic mode functions. This algorithm is capable of separating the linear and non-linear seismic events into linear modes and optimizing the linear patterns within amplitude frequency modes. The noisy modes are selected and attenuated by an adaptive self-guided filter. The GMD method is experimentally verified with a strong theoretical background to address the directional features of the image. ASGF is an exceptional edge-preserving filter and hence the hybrid algorithm could utilize the advantages of both methods. Higher the signal-to-noise ratio, improving the resolution of the image and preserving the directional properties and edges of the seismic events are the paramount characteristics of the proposed model. The simulating results on both synthetic and real seismic data proved the technique is more promising compared to the existing methods. [ABSTRACT FROM AUTHOR]

The system identification under non-negative constraints problem is a common and important one in the real-life problems. A non-negative least mean squares algorithm was proposed to address such problems. However, it suffers from slow and unbalanced convergence. Motivated by the introduction of non-Newtonian gradient in least mean squares algorithm can accelerate the convergence. In this paper, an efficient non-negative least mean squares algorithm based on q-gradient is developed. At first, the q-gradient of the mean square error cost function is derived from the definition of Jackson's derivative. Then, the modified algorithm is developed by replacing the conventional gradient with the q-gradient on a fixed-point iteration scheme using Karush-Kuhn-Tucker condition. Simulations for stationary and non-stationary system identification are conducted to illustrate the effectiveness of the developed algorithm for constrain the system parameters to be non-negative and accelerate the convergence speed. Besides, it can balance the convergence rate for widely distributed parameters, especially for the convergence of small parameters. For practical application, the accuracy of system identification when the order of the adaptive filter is larger than the order of the system is also verified by the simulation. • An efficient non-negative least mean squares algorithm based on q-gradient is developed. • The algorithm builds on a fixed-point scheme with KKT conditions using q-gradient of cost function. • The algorithm can improve the convergence speed and the balance of the convergence of parameters. [ABSTRACT FROM AUTHOR]

The presence of noise is a pervasive issue that significantly impacts the performance of adaptive filtering algorithms. To address this challenge, the bias compensation technique has recently emerged, involving the incorporation of an additional term into the update equation. Despite the apparent simplicity of the bias-compensated least-mean-squares algorithm, conducting a comprehensive theoretical analysis of its performance is a complex task. In this paper, we undertake an exact expectation analysis to demonstrate the asymptotic unbiasedness of the algorithm, even when disregarding the commonly assumed theory of independence between adaptive coefficients and input data. Furthermore, to improve the understanding of the algorithm's stability, we employ a stochastic model that assumes independence between the radial and angular distributions of the input vector. The resulting model is intricate, necessitating heuristic approximations to derive practical insights. Notably, our analysis reveals that the upper bound on the step size of the bias-compensated least-mean-squares algorithm consistently remains smaller than that of the least-mean-squares algorithm. These findings gain robust support from extensive simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose the bionic tracking-containment control based on smooth transition in communication, which is inspired by wolf hunting. To realize the hunting target process, we concentrate on the transition from tracking control to containment control of the swarm system by using backstepping method. The smooth transition of switching communication topology is introduced to realize bionic switching. We design a smooth switching function, which can avoid the negative influence of instant switch. Due to the input saturation and smooth switching function, it is challenging to design the tracking-containment controller with nonlinear dynamics and external disturbances. To overcome these difficulties, we design the tracking-containment control based on the adaptive backstepping method, which includes n command filters and one adaptive neural network to improve the control performance. The swarm system is proved stable under the Lyapunov stability criterion. Finally, simulation is carried out to verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• A generalized Student's t-based kernel function, and a new kernel adaptive filtering (KAF) algorithm named generalized Student's t kernel adaptive filter (GStKAF) are designed under the KMPE error criterion for indoor positioning under the IoT framework. • The L1-norm is proposed as the penalty to embed into the GStKAF and the resulting sparse GStKAF (SGStKAF) can provide more compact size of the neural networks. • Three experimental results show that the proposed SGStKAF has good robustness against the mixed noise consisting of abrupt noise and Gaussian noise while maintaining the high accuracy performance. The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student's t kernel (GSt) and a resulting sparse generalized Student's t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p -power error criterion (KMPE) with the L 1 -norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student's t kernel can suppress the abrupt noise effectively. Secondly, the L 1 -norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L 1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments. [ABSTRACT FROM AUTHOR]

Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter’s recursion algorithms. The filters’ coefficients and algorithms’ underlying variables are fully operated using SOPOT arithmetic in the whole iterative process. More specifically, one evaluates convergence rate, numerical stability, and accuracy since using few signed-powers-of-two (SPT) terms propagates numerical errors during the adaptive cycle that may impair the algorithm behavior. The SOPOT approximations are obtained through the technique known as Matching Pursuits with Generalized Bit-Plane (MPGBP) algorithm, with notable cost-performance trade-off and low computational complexity. Results are provided for the Least-Mean-Squares (LMS), the Normalized Least-Mean-Squares (NLMS) and the Recursive-Least-Squares (RLS) algorithms, considering adaptive filters employed for system identification and change detection. [ABSTRACT FROM AUTHOR]

Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has been successfully applied in robust signal processing and machine learning. To date, many robust learning algorithms have been devised based on the minimum error entropy (MEE) criterion, and the Gaussian kernel function is always utilized as the default kernel function in these algorithms, which is not always the best option. To further improve learning performance, two concepts using a mixture of two Gaussian functions as kernel functions, called mixture error entropy and mixture quantized error entropy, are proposed in this paper. We further propose two new recursive least-squares algorithms based on mixture minimum error entropy (MMEE) and mixture quantized minimum error entropy (MQMEE) optimization criteria. The convergence analysis, steady-state mean-square performance, and computational complexity of the two proposed algorithms are investigated. In addition, the reason why the mixture mechanism (mixture correntropy and mixture error entropy) can improve the performance of adaptive filtering algorithms is explained. Simulation results show that the proposed new recursive least-squares algorithms outperform other RLS-type algorithms, and the practicality of the proposed algorithms is verified by the electro-encephalography application. [ABSTRACT FROM AUTHOR]

This article proposes an affine-projection-like maximum correntropy (APLMC) algorithm for robust adaptive filtering. The proposed APLMC algorithm is derived by using the objective function based on the maximum correntropy criterion (MCC), which can availably suppress the bad effects of impulsive noise on filter weight updates. But the overall performance of the APLMC algorithm may be decreased when the input signal is polluted by noise. To compensate for the deviation of the APLMC algorithm in the input noise interference environment, the bias compensation (BC) method is introduced. Therefore, the bias-compensated APLMC (BC-APLMC) algorithm is presented. Besides, the convergence of the BC-APLMC algorithm in the mean and the mean square sense is studied, which provides a constraint range for the step-size. Computer simulation results show that the APLMC, and BC-APLMC algorithms are valid in acoustic echo cancellation and system identification applications. It also shows that the proposed algorithms are robust in the presence of input noise and impulse noise. [ABSTRACT FROM AUTHOR]

In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances (PMNC) in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian–Gamma mixture distributions. The VB inference is used to perform the state and PMNC simultaneously. Simulations show that the effectiveness of the proposed method with inaccurate noise covariances in the presence of outliers environments. [ABSTRACT FROM AUTHOR]

This article proposes a prescribed adaptive backstepping scheme with new filter-connected switched hysteretic quantizer (FCSHQ) for switched nonlinear systems with nonstrict-feedback structure and time-delay. The system model is subjected to unknown functions, unknown delays, and unknown Bouc–Wen hysteresis nonlinearity. The coexistence of quantized input and actuator hysteresis may deteriorate the shape of hysteresis loop and, consequently, fail to guarantee the stability. To deal with this issue, a new FCSHQ is introduced to smooth the input hysteresis. This adaptive filter also provides us a degree of freedom at choosing the desired communication rate. The repetitive differentiations of virtual control laws and existing a lot of learning parameters in the neural network (NN)-based controller may result in an algebraic loop problem and high computational time, especially in a nonstrict-feedback form. This challenge is eased by the key advantage of NNs’ property where the upper bound of the weight vector is employed. Then, by an appropriate Lyapunov–Krasovskii functional, a common Lyapunov function is presented for all subsystems. It is shown that the proposed controller ensures the predefined output tracking accuracies and boundedness of the closed-loop signals under any arbitrary switching. Finally, the proposed control scheme is verified on a practical example where simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

For conventional backstepping control, the analytical calculation of certain virtual controller is required in each step, so when system order increases, the computational burden also increases. An adaptive command filtered fuzzy synchronization approach is implemented for strict feedback fractional-order chaotic systems (FCSs) in this paper. The computational explosion in the backstepping is solved through a fractional-order command filter (FCF). The filtering approximation errors arising from the FCF are overcame by a error compensation mechanism (ECM). In every step, a fuzzy system is introduced to process system uncertainties. The stability of synchronization errors of a strict feedback FCS is ensured based on fractional Lyapunov stability criteria. Finally, numerical simulation examples illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC). [ABSTRACT FROM AUTHOR]

This article studies delay compensation in consensus control under undirected graphs via output feedback. The dynamics of each node involved in the network is subject to an unknown bounded actuator delay. A transport partial differential equation is employed to model the dynamics of the delay state, and a filter with adaptive control gains is proposed using the available relative output information of neighboring nodes. Based on the state of the filter, the estimation delay state, and the delay estimation, we propose two distributed adaptive output feedback consensus protocols. The first one employs a delay-adaptive predictor and guarantees local consensus, whereas the second one, replacing the delay estimation by a constant, is proven to be a global consensus controller provided that the delay mismatch is in a small compact set. Simulation results performed on a group of neutrally stable systems are presented to validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

A full performance analysis of complex normalized subband adaptive filter (CNSAF) algorithm will provide guidelines for designing the adaptive filter. However, because of the noncircular characteristic of complex-value signal, the complementary mean-square performance analysis of the CNSAF algorithm has not been presented in the literature. In order to give the detailed theoretical expressions of the CNSAF algorithm, the present study first analyzes the mean-square deviation (MSD) with the energy-conservation method, and then the complementary mean-square derivation (CMSD) behavior is given using pseudo-energy-conservation method. Analytical expressions are obtained for the transient MSD and CMSD of the CNSAF algorithm. Also, the steady-state MSD and CMSD are predicted based on the closed-form expressions. Besides, the analysis results are not constrained by the distribution of input signals. Finally, simulation results obtained for diffferent inputs present a good consistence with the analytical results. [ABSTRACT FROM AUTHOR]

In this study, a self-organizing interval type-3 fuzzy logic system (SO-IT3FLS) with a new learning algorithm is presented. An adaptive kernel size using fuzzy systems is introduced to improve the robustness of conventional correntropy based Kalman filters against non-Gaussian noise. The maximum correntropy Kalman filter (MCKF) and maximum correntropy unscented Kalman filter (MCUKF) with the proposed adaptive fuzzy kernel size are reformulated to optimize both rule and antecedent parameters, respectively. In addition to the rule parameters, the proposed membership function (MF) parameters and the level of α -cuts are also optimized. Five simulation examples with real-world data sets are given for examination. The simulations show that the introduced SO-IT3FLS and learning algorithm result in better accuracy in contrast to the other kind of fuzzy neural networks and conventional learning techniques. Furthermore, it is verified that the robustness of the proposed learning method against non-Gaussian noise is improved in contrast to the conventional Kalman filter, maximum correntropy Kalman filter and unscented Kalman filter. [ABSTRACT FROM AUTHOR]

This paper investigates a class of nonlinear systems with actuator fault. In particular, fuzzy logic systems have been used to approximate the unknown nonlinear functions, backstepping procedure is adopted to design controller for the system with mismatched condition, command filter is utilized to eliminate the explosion of complexity of the backstepping and also to compensate the output of a filter subjected to the derivative of the virtual control. The stability of the closed-loop system and the convergence of the tracking error are proved via Lyapunov Theorem. In addition, two numerical simulation examples are illustrated the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multi-rail power converters. The proposed approach uses an adaptive update rate as opposed to the fixed update rate used in conventional adaptive filters. Also, the step size/forgetting factors vary at different iteration stages. As a result, a reduced computational burden and faster model update can be achieved. Besides, recursive algorithms, such as Recursive Least Square (RLS), Fast Affine Projection (FAP) and Kalman Filter (KF), contain two important updates per iteration cycle; Covariance Matrix Approximation (CMA) update and Gradient Vector (GV) update. Usually, the CMA update requires the greater computational effort than the GV update. Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the CMA, calculated from the last iteration cycle for the current update can result in computational cost savings for real-time system identification. In this paper, both iteration rate adjustment and CMA re-cycling are combined and applied to simultaneously identify the power converter models in a three-rail power conversion architecture. [ABSTRACT FROM AUTHOR]