This brief proposes a novel perspective for solving single input and multiple output (SIMO) nonlinear systems with multi-cost function adaptive algorithm. Based on Euler polynomials, the Euler filter ...with finite memory is proposed to construct a filter bank, which realizes the accurate identification of high-dimensional nonlinear systems. To improve the algorithm performance, a multi-cost function is proposed. Moreover, the mean-square stability and steady-state performance of the proposed method are rigorously analyzed. The typical FitzHugh-Nagumo (FHN) system, which fiercely exhibits chaotic dynamics, is considered for justifying the performance of this new algorithm. Numerical results highlight the solution accuracy via comparison with existing approaches.
The Internet of Vehicles (IoV) plays a central role in intelligent transportation systems. Components, such as motor and transmission in the vehicle may produce noise, which seriously affects ...comfort. Therefore, vehicle manufacturers attach great importance to active noise control (ANC) technology. However, such an ANC system may have some nonlinear distortions in practical, thereby the nonlinear ANC (NANC) system is warranted. Moreover, we consider using IoV for rational resource allocation and record historical data for fault diagnosis, early warning, etc. So far, no work on NANC in the IoV environment is reported. In this article, based on the Hermite polynomial, a class of functional link artificial neural network (FLANN) algorithms is developed for NANC. The first proposed algorithm, called filtered-h least mean <inline-formula> <tex-math notation="LaTeX">{\mathcal {L}}_{p} </tex-math></inline-formula>-norm (FhLMP), incorporates the <inline-formula> <tex-math notation="LaTeX">{\mathcal {L}}_{p} </tex-math></inline-formula>-norm to obtain reliable performance. To further enhance the performance, the recursive FhLMP (RFhLMP) and hyperbolic recursive FhLMP (HRFhLMP) algorithms are designed by formulating two recursive structures. The proposed RFhLMP algorithm takes the filter output as part of the input and is expanded by the Hermite FLANN. The HRFhLMP algorithm activates the output by a hyperbolic tangent function and then recursively returns the activated output to the filter input. Simulations verify the improvement of the proposed algorithms for the NANC system.
Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly ...developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)-based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.
Part I of this paper reviewed the development of the linear active noise control (ANC) technique in the past decade. However, ANC systems might have to deal with some nonlinear components and the ...performance of linear ANC techniques may degrade in this scenario. To overcome this limitation, nonlinear ANC (NLANC) algorithms were developed. In Part II, we review the development of NLANC algorithms during the last decade. The contributions of heuristic ANC algorithms are outlined. Moreover, we emphasize recent advances of NLANC algorithms, such as spline ANC algorithms, kernel adaptive filters, and nonlinear distributed ANC algorithms. Then, we present recent applications of ANC technique including linear and nonlinear perspectives. Future research challenges regarding ANC techniques are also discussed.
Functional link artificial neural network (FLANN) has received much attention due to its wide applicability. The Van der Pol-Duffing oscillator (VdPDO)-based nonlinear systems, which own complex ...dynamical behaviors, identification of such nonlinear model is vital. This paper exploits the nonlocality of fractional calculus, aiming to enhance the identification accuracy of the VdPDO-based nonlinear systems. The proposed combined FEM-LMS (CFEM-LMS) algorithm, which is based on the FLANN structure, convexly combines the least mean square (LMS) algorithm and the newly proposed fractional-order error modified LMS (FEM-LMS) algorithm. The CFEM-LMS algorithm has improved performance and can dynamically adapt to the nonlinearity of the system. As an added contribution, a novel mixing parameter adaptation criterion is proposed for performance improvement. Extensive simulation results in the context of VdPDO-based nonlinear system identification demonstrate the superiority of the proposed algorithm as compare to state-of-the-art approaches.
•A class of DFLMS algorithm is derived for distributed nonlinear networks, in which four types of DFLNs are respectively used.•Based on the probit regression, the proposed CRDFLAF algorithm corrects ...the bias of censored measurements.•The stability condition of the learning rate is derived, and the corresponding computational complexity is provided.•Simulation results of two types of distributed nonlinear networks verify the effectiveness of the proposed algorithms.
Wireless sensor network (WSN) is an important part of the Internet of Things (IoT) and has emerged in various new forms, such as smart home, smart city, and intelligent manufacturing system. Due to its high reliability, distributed estimation over nonlinear WSNs is one of the most active fields in recent years. In this paper, a novel distributed functional link least mean square (DFLMS) algorithm based on rblackthe diffusion strategy is proposed, in which the diffusion functional link network (DFLN) is used to model the nonlinear dynamic behavior of the distributed system. In particular, by using different orthogonal polynomials, we develop four types of DFLNs, i.e., trigonometric DFLN (TDFLN), Legendre DFLN (LDFLN), Chebyshev DFLN (CDFLN), and Hermite DFLN (HDFLN). However, the censored measurement caused by the range of sensors brings great challenges to the traditional distributed nonlinear estimation. To tackle this problem, a censored regression-distributed functional link adaptive filtering (CR-DFLAF) algorithm is further proposed. Compared with the DFLMS algorithm, the CR-DFLAF algorithm can compensate the estimated bias in the CR scenario at the price of slightly increased computational complexity. Simulations involving two distributed nonlinear networks verify the effectiveness of the proposed algorithms.
This paper introduces a new class of nonlinear filters for nonlinear acoustic echo cancellation (NLAEC) based on Hermite nonlinear filters (HNFs), which is a sub-class of linear-in-the-parameters ...nonlinear filters (LIPNFs). Specifically, the basis functions of HNFs include cross-terms of the expanded inputs at different time instants, and are mutually orthogonal for white normally distributed input signals. Although HNFs yield good performance for NLAEC, they suffer from high computational complexity. To tackle this problem, a computationally efficient pipelined variant of HNFs is introduced. The pipelined HNFs (PHNFs) include a nonlinear part followed by a linear one such that the input space is first expanded nonlinearly and then cast by a linear mapping to the output space. Experimental results using both synthesized and real data, and involving several nonlinear scenarios show the effectiveness of the proposed approach.
•Innovative Approach: The paper introduces a groundbreaking paradigm shift in nonlinear adaptive filters.•Computational Analysis: Computational analysis illustrates the complexity of the proposed method.•Validation via Simulations: Simulations demonstrate the effectiveness of the PHNFs approach.
Abstract Microvesicles are formed under many circumstances, especially in atheromatous plaques. Erythrocyte-derived microvesicles (ErMVs) have been proved to promote atherosclerosis by promoting a ...hypercoagulation, mediating inflammation and inducing cell adhesion. Several clinical studies have reported potential roles of ErMVs in cardiovascular disease diagnosis, but the current understanding of ErMVs remains insufficient. In this paper, we will review current research on the formation and degradation of ErMVs and the possible effects of ErMVs in atherosclerosis, discuss potential clinical applications in cardiovascular disease, and hope to raise awareness of the relation with atherosclerosis.
Active noise control (ANC) is gaining attention for attenuating noise from a remote location. Considering the problem of nonlinear active noise control (NLANC) at a virtual location, a robust ...filtered-s subband adaptive filtering algorithm based on the <inline-formula><tex-math notation="LaTeX">q</tex-math></inline-formula>-gradient maximum correntropy criterion (RFsSAF-qMCC) is proposed in this paper. The proposed RFsSAF-qMCC algorithm develops the functional link artificial neural network (FLANN)-SAF structure as the controller, and embeds the MCC with the concept of q -gradient, thereby improving the convergence speed in the impulsive environment. To solve the trade-off between fast convergence and low noise residue caused by the fixed q -gradient, a variable q -gradient algorithm, termed as RFsSAF-vqMCC, is further developed. As an additional contribution, the convergence behavior of the proposed RFsSAF-qMCC and RFsSAF-vqMCC algorithms is analyzed. Simulation results corroborate the effectiveness of the proposed algorithms as compared to state-of-the-art algorithms.
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