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Routing algorithms can improve network performance by maximizing routing adaptiveness but can be problematic in the presence of endpoint congestion. Tree-saturation is a well-known behavior caused by endpoint congestion. Adaptive routing can, however, spread the congestion and result in thick branches of the congestion tree — creating Head-of-Line (HoL) blocking and degrading performance. In this...
In this paper, an adaptive adjustment method for the kernel parameter used in the kernel adaptive filters (KAFs) is proposed. The KAF is one of the linear-in-the-parameters (LIP) nonlinear filters, and is based on the kernel method used in machine learning. Typically, the Gaussian kernel function is used, but there is no effective method for automatically adjusting its parameter that influences the...
In this paper, a composite integral terminal sliding mode surface is developed, and an adaptive synchronization control scheme is proposed accordingly for multiple robotic manipulators with actuator saturation. By combining the terminal sliding mode with integral terms, the presented scheme is able to reduce the static tracking errors and improve the synchronization performance. Moreover, the system...
This paper investigates the finite-time tracking problem for nonholonomic mobile robots despite of unknown system disturbances. In the proposed tracking control, a novel sliding mode controller is given at torques level such that both the position and the orientation tracking errors convergence to zero within finite time. In particular, by employing the adaptive method during the robot's moving process,...
Striking hostile ground targets for air force is a crucial way to achieve air superiority in modern warfare. For the maximum efficiency of limited munitions, the optimal weapon target assignment (WTA) scheme should be developed. Aimed at the fact that lots of approaches have been presented for the WTA problem of stationary targets, while less for the ones of moving targets, this paper proposes an...
The dynamics of two-word naming game incorporating the influence of biased assimilation is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he will refuse to accept the conveyed word with a predefined probability, if it is different from his own current memory. Secondly,...
This paper presents the analysis and design of an adaptive parameter estimator for power electronics circuits. Adaptive parameter estimation has been demonstrated to be a useful technique for enabling novel controls, monitoring, and fault diagnosis techniques in power electronics systems. We present an analysis of factors that affect the performance, stability, and feasibility of a gradient-type parameter...
In this paper we revisit the well known and popular Normalized Subband Adaptive Filter (NSAF). Based on an analysis of the algorithm in the mean and using an analysis strategy presented in [1], we find that the NSAF can be seen as a Richardson iteration applied to a preconditioned augmented Wiener-Hopf equation. This equation is formulated in such a way that its convergence speed can be predicted...
We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network...
This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems...
In this paper, a recurrent wavelet neural network(RWNN) control system based on chaotic series adaptive particle swarm optimization (PSO) is proposed to control the PMSM drive system. First, the RWNN control system is developed for the control of PMSM. Moreover, the on-line learning algorithms of the connective weights, translations and dilations of the RWNN are derived using back-propagation(BP)...
With the growing popularity of big-data applications, Data Center Networks increasingly carry larger and longer traffic flows. As a result of this increased flow granularity, static routing cannot efficiently load-balance traffic, resulting in an increased network contention and a reduced throughput. Unfortunately, while adaptive routing can solve this load-balancing problem, network designers refrain...
Estimation of small tap-coefficients of large sparse system using μ-law based proportionate normalized least square (MPNLMS) algorithm yields slow converges, since the proportionality of these coefficients is ignored in the updated process. The individual activation factor-MPNLMS (IAF-MPNLMS) algorithm solves this problem by assigning new gain distribution factor while updating the tap-coefficients...
In this paper we present a generic distributed weight adaptation framework to optimize some network observables of interest. We focus on the algebraic connectivity λn, the spectral radius λn, the synchronizability λn/λ2, or the total effective graph resistance ω of undirected weighted networks, and describe distributed systems for the estimation of these functions and their derivatives for on-line...
This paper studies the problem of distributed affine projection algorithm (DAPA) for distributed estimation in the sparse system identification over adaptive networks. Compared with the distributed least mean square (DLMS) algorithm, DAPA reuses previous input vectors in order to increase the convergence speed when the input signal is colored. We propose two versions of the DAPA for sparse system...
Online communities promise a new era of flexible and dynamic collaborations. However, these features also raise new security challenges, especially regarding how trust is managed. In this paper, we focus on situations wherein communities participants collaborate with each others via software agents that take trust decisions on their behalf based on policies. Due to the open and dynamic nature of Online...
We consider stabilization problem for n-dimensional kinematic nonholonomic systems with m-inputs. The control objective in exponential stabilization is to force the system states x ∊ Rn from an arbitrary initial state x0 to origin with a finite convergence rate γ. We employ adaptive back stepping to stabilize the nonholonomic system. Adaptive back stepping seeks to stabilize n system states in n +...
An adaptive iterative learning control (AILC) is proposed to release the requirement of repetitiveness for a class of multiple-input multiple-output nonlinear systems. Based on high-order internal model, the time-iteration-varying law of parametric uncertainty is expressed. Furthermore, reference trajectory and initial state are varying from iteration to iteration randomly. The asymptotic convergence...
To improve the dynamic response characteristics of permanent magnet synchronous motor (PMSM) servo system, an inertia identification method based on the theory of model reference adaptive system (MRAS) has been researched. A novel inertia identification method using genetic algorithm (GA) is proposed for requirements of rapid convergence and high precision. This method takes advantage of the global...
In this paper, we focus on the problem of removing noise in the acoustic domain. To this end, we introduce a class of hybrid nonlinear spline filters, which are designed as a cascade of an adaptive spline function and a single layer adaptive nonlinear network. The adaptive nonlinear networks employed in this work are the functional link network and the even mirror Fourier nonlinear network. Suitable...
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