Search

In this paper, convergence capability of the conventional fractional order gradient methods (FOGMs) is analyzed and a new FOGM with guaranteed and faster convergence ability is proposed. In particular, we further consider the case that the derivative order varies between 1 and 2, and give some discussion on how to determine the derivative order in the algorithm settings. Simulation results from a number of numerical examples are provided to illustrate the approaches. [ABSTRACT FROM AUTHOR]

This paper presents a synthetic algorithm for tracking a moving object in a multiple-dynamic obstacles environment based on kinematically planar manipulators. By observing the motions of the object and obstacles, Spline filter associated with polynomial fitting is utilized to predict their moving paths for a period of time in the future. Several feasible paths for the manipulator in Cartesian space can be planned according to the predicted moving paths and the defined feasibility criterion. The shortest one among these feasible paths is selected as the optimized path. Then the real-time path along the optimized path is planned for the manipulator to track the moving object in real-time. To improve the convergence rate of tracking, a virtual controller based on PD controller is designed to adaptively adjust the real-time path. In the process of tracking, the null space of inverse kinematic and the local rotation coordinate method (LRCM) are utilized for the arms and the end-effector to avoid obstacles, respectively. Finally, the moving object in a multiple-dynamic obstacles environment is thus tracked via real-time updating the joint angles of manipulator according to the iterative method. Simulation results show that the proposed algorithm is feasible to track a moving object in a multiple-dynamic obstacles environment. [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]

Extinction coefficient is the base parameter of visibility measurement, its measurement error determines the accuracy of the visibility meter. In the paper, the error sources are analyzed, and a new optical structure is proposed by adding the optical compensator. For the new structure, the measurement model based on boundary constraints is provided to reduce measure errors. Based on the new model, use an improved algorithm to solve the error convergence problem. The new algorithm use exponent arctangent function as base function, three parameters i, j and k control the contour of the function. With simulation tool, the optimization values are obtained. The experimental results show that the extinction coefficient relative error is reduced by 18% in the entire measurement region, the fluctuations of the relative error is reduced to 7.3%. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, Java programming with applets for internet-based control education of two mechanical systems are presented. First, a new multi-objective optimization method is applied to obtain the Pareto frontiers of some non-commensurable objective functions in the design of linear state feedback controllers for an inverted pendulum and a ball–beam system. Second, the simulations of the problems were developed with Java applets and its results are given. The obtained results and analyses demonstrate that this multi-objective method presented in this paper operates very well in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. [Copyright &y& Elsevier]

Abstract: This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section . Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors. In order to evaluate the performance of the proposed algorithm, it is applied to a system consisting of 13 and 40 thermal units whose fuel cost function is calculated by taking account of the effect of valve-point loading. Simulation results demonstrate the superiority of the proposed algorithm compared to other optimization algorithms presented in literature. [Copyright &y& Elsevier]

Abstract: This paper studies the distributed formation control problem for multiple fractional-order systems under dynamic interaction and with absolute/relative damping. In the context of this paper, formation control means that a group of systems reaches the desired state deviations via a local interaction. We first study a formation control algorithm in the case of a directed dynamic network topology. The convergence conditions on both the network topology and the fractional orders are presented. When the fractional-order satisfies , sufficient conditions on the network topology are given to ensure the formation control. We then propose fractional-order formation control algorithms with absolute/relative damping and study the conditions on the network topology and the control gains such that the formation control will be achieved under a directed fixed network topology. The final equilibria are also given explicitly. Finally, several simulation examples are presented as a proof of concept. [Copyright &y& Elsevier]

This paper investigates how intelligent an agent may utilize a Q-learning approach, a simulation-based stochastic technique, to make optimal dynamic packaging decision in e-retailing setting. When the practical application of dynamic packaging involves a large number of products, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. This paper first describes the state-dependent and event-driven nature of the dynamic packaging problem with a Markov decision process model, then proposes a states generalization approach based on distortion measure, and finally puts forward a heuristic based exploration/exploitation policy which is used to improve the convergence in Q-learning. We validate our approach in a simulated test. [Copyright &y& Elsevier]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [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]

In industrial applications, many batch/repetitive systems are constructed by two or more subprocesses connected in serial to perform a given task repetitively or periodically, termed as cascaded batch/repetitive processes. In this paper, by introducing a two-dimensional (2D) cost function, an integrated design method of a novel cascade control scheme is developed for this kind of process under the 2D model predictive iterative learning control infrastructure. The resulted cascade control scheme essentially consists of a real-time cascade model predictive control along the time direction as the inner-loop control and an iterative learning control (ILC) along the cycle direction as the outer-loop control. The proposed design algorithm not only realizes an integrated design for all control loops but also guarantees optimal control in terms of the 2D control performance. The convergence criterion of the closed-loop control system along the cycle index is also investigated. Compared to the conventional cascade control schemes, the proposed algorithm is essentially a 2D feedback control with superior control performances which are clearly illustrated by the simulation results on the injection molding process. [ABSTRACT FROM AUTHOR]

The development of Schramm-Loewner evolution (SLE) as the scaling limits of discrete models from statistical physics makes direct simulation of SLE an important task. The most common method, suggested by Marshall and Rohde |MR05|, is to sample Brownian motion at discrete times, interpolate appropriately in between and solve explicitly the Loewner equation with this approximation. This algorithm always produces piecewise smooth non self-intersecting curves whereas SLEκ has been proven to be simple for κ ∊ [0,4], self-touching for κ ∊ (4,8) and space-filling for κ > 8. In this paper we show that this sequence of curves converges to SLEκ for all κ ≠ 8 by giving a condition on deterministic driving functions to ensure the sup-norm convergence of simulated curves when we use this algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence; however, it suffers from a biased steady-state solution when the reference signal lags behind the desired signal or the adaptive filter is of insufficient length. This paper proposes a unified framework for the FBLMS algorithm, which can be used to comprehensively analyze its steady-state behavior. Furthermore, a modified FBLMS algorithm with guaranteed optimal steady-state performance is proposed based on the framework. Simulations are carried out to demonstrate the benefit of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Parareal is a recent time parallelization algorithm based on a predictor–corrector mechanism. Recently, it has been applied for the first time to a fully-developed plasma turbulent simulation, and a qualitative understanding of how parareal converges exists for this case. In this paper, we construct an analytical framework of the process of convergence that should be applicable to parareal simulations of general turbulent systems. This framework allows one to gain a quantitative understanding of the dependence of the convergence on the physics of the problem and the choices that must be made to implement parareal. The analytical knowledge provided by this new framework can be used to optimize the implementation of parareal. We illustrate the inner workings of the framework and demonstrate its predictive capabilities by applying it to the modeling of the parareal convergence of drift-wave plasma turbulent simulations. [Copyright &y& Elsevier]

Focus on the electricity load forecasting in energy-intensive enterprise, a novel Boosting forecasting method based on geometric conversion is proposed. This method extends the AdaBoost algorithm to the regression problem. In this method, a regression problem such as load forecasting is converted to a binary classification problem through geometric operation. Then confidence-rated AdaBoost is implemented on this classification problem and an ensemble separating plane is obtained. This paper proves that such a plane is identical to a regression function for original load forecasting problem, and also proves that the proposed method has theoretical convergence. Simulation results state that compared to some popular single forecasting models, the proposed method improves the accuracy of the load forecasting in energy-intensive enterprise. [ABSTRACT FROM AUTHOR]

This paper considers flocking to the virtual leader in network of agents with double-integrator. A locally linear algorithm is employed which guarantees exponential flocking to the virtual leader. A lower bound for flocking rate is calculated which is independent of the ini-tial conditions. Simulations are provided to validate the result and it is shown that the calculated rate is not over bound the actual conver-gence rate. The effect of coefficients of algorithm is investigated and it is shown that the similar results can be inferred from the calcu-lated formula for the convergence rate. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the convergence properties of the least squares parameter estimation algorithm for multivariable systems that can be parameterized into a class of multivariate linear regression models. The performance analysis of the algorithm by using the stochastic process theory and the martingale convergence theorem indicates that the parameter estimation errors converge to zero under weak conditions. The simulation results validate the proposed theorem. [Copyright &y& Elsevier]

Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program(IQP) routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithmcan reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR) demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Many iterative approaches in the field of system identification for control have been developed. Although successful implementations have been reported, a solid analysis with respect to the convergence of these iterations has not been established. The aim of this paper is to present a thorough analysis of a specific iterative algorithm that involves nonparametric -norm estimation. The pursued methodology involves a novel frequency domain approach that addresses both additive stochastic disturbances and input normalization. The results of the convergence analysis are twofold: (1) the presence of additive disturbances introduces a bias in the estimation procedure, and (2) the iterative procedure can be interpreted as experiment design for -norm estimation, revealing the value of iterations and limits of accuracy in terms of the Fisher information matrix. The results are confirmed by means of a simulation example. [Copyright &y& Elsevier]

Abstract: Bayesian inference methods are used extensively in the analysis of Generalized Linear Mixed Models (GLMMs), but it may be difficult to handle the posterior distributions analytically. In this paper, we establish the asymptotic normality of the joint posterior distribution of the parameters and the random effects in a GLMM by using Stein’s Identity. We also show that while incorrect assumptions on the random effects can lead to substantial bias in the estimates of the parameters, the assumed model for the random effects, under some regularity conditions, does not affect the asymptotic normality of the joint posterior distribution. This motivates the use of the approximate normal distributions for sensitivity analysis of the random effects distribution. We additionally illustrate that the approximate normal distribution performs reasonably using both real and simulated data. This creates a primary alternative to Markov Chain Monte Carlo (MCMC) sampling and avoids a wide range of problems for MCMC algorithms in terms of convergence and computational time. [Copyright &y& Elsevier]

Abstract: Variable order affine projection algorithms have been recently presented to be used when not only the convergence speed of the algorithm has to be adjusted but also its computational cost and its final residual error. These kind of affine projection (AP) algorithms improve the standard AP algorithm performance at steady state by reducing the residual mean square error. Furthermore these algorithms optimize computational cost by dynamically adjusting their projection order to convergence speed requirements. The main cost of the standard AP algorithm is due to the matrix inversion that appears in the coefficient update equation. Most efforts to decrease the computational cost of these algorithms have focused on the optimization of this matrix inversion. This paper deals with optimization of the computational cost of variable order AP algorithms by recursive calculation of the inverse signal matrix. Thus, a fast exact variable order AP algorithm is proposed. Exact iterative expressions to calculate the inverse matrix when the algorithm projection order either increases or decreases are incorporated into a variable order AP algorithm leading to a reduced complexity implementation. The simulation results show the proposed algorithm performs similarly to the variable order AP algorithms and it has a lower computational complexity. [Copyright &y& Elsevier]

This paper defines an outlier and a measure that an outlier does not fit the theoretical model in the regression problems, studies the relationship between the theoretical model and the regression model in the regression problems, proposes and verifies an approximate theorem in which one-by-one deletes outlier and constructs SVR to approximate its theoretical model, draws an algorithm of detecting outliers in the SVR problems based on the approximate theorem, and analyzes the convergence and effectiveness of the algorithm in the theory. Next, it combines the step-by-step search algorithm were proposed by authors and the former detecting outlier algorithm to improve it, and brings forward an algorithm detecting outliers in SVR based on large-scale samples. Then, its analysis in theory shows the improved algorithm is convergent and effective too. Finally, its simulation results show that the detecting outlier algorithms are effective and robust, using samples produced by two test functions and in UCI dataset. [ABSTRACT FROM AUTHOR]

Short-term hydrothermal generation scheduling aims at determining optimal hydro and thermal generations to achieve minimum fuel cost of thermal plants for a 1 day or a 1 week while meeting various hydraulic and electric system constraints. The problem is viewed as a complex and nonlinear hard problem considering valve-points effects and transmission losses with a set of operation operational and physical constraints. This paper presents a novel effective differential real-coded quantum-inspired evolutionary algorithm (DRQEA) for solving this complicated problem. Some improvements like real-coded rule, adaptive differential mutation and crossover mechanism are proposed in DRQEA to enhance the global search ability in continuous space. Meanwhile, various constraints are handled effectively by using heuristic strategies designed by their characteristics. The effectiveness of the proposed approach is demonstrated on two hydrothermal test systems, which consist of two sub-systems: hydro sub-system and thermal sub-system. The obtained results of the proposed approach are compared with other methods, and simulation and comparison results clearly show that DRQEA is able to provide better solution than other reported methods, both in the solution quality and the convergence speed. The proposed algorithm can also apply to other dynamic optimization problem with nonlinear and non-convex characteristics in power system. [ABSTRACT FROM AUTHOR]

Abstract: Accurate kinetic models are of great significance for the simulation and analysis for hydrogen fuel cells. The proton exchange membrane (PEM) fuel cell is a complex nonlinear, multi-variable system. The mathematical modeling of PEM fuel cell usually leads to nonlinear parameter estimation problems which often contain more than one minimum. In this paper, a novel bio-inspired P systems based optimization algorithm, named BIPOA, is proposed to solve PEM fuel cell model parameter estimation problems. In BIPOA, the nested membrane structure and new rules such as adaptive mutation rule, partial migration rule and autophagy rule are combined to improve the algorithm''s global search capacities and convergence accuracy. Studies on some benchmark test functions indicate that the BIPOA outperforms the other two methods (PSOPS and GAs) in both convergence speed and accuracy. In addition, experimental results reveal that the model predictive outputs are in better agreement with the actual experimental data. Therefore, the BIPOA is a helpful and reliable technique for estimating the PEM fuel cell model parameters and is available to other complex parameter estimation problems of fuel cell models. [Copyright &y& Elsevier]

Beamforming with sparse constraint has shown important performance improvement. In this paper, a robust least squares constant modulus beamforming with sparse constraint is proposed. The proposed approach shows a faster convergence performance, lower sidelobe level, and better steady-state output signal-to-interference-plus-noise ratio (SINR) than that of the conventional linearly constrained least squares constant modulus algorithm, especially in the presence of mismatches between the actual and presumed steering vectors. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained. [ABSTRACT FROM AUTHOR]

In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA) based on the arguments of energy conservation. Although the APA and its convergence analysis have been widely studied, the dependency of weight-error vector on past noise is usually neglected for simplicity. To obtain accurate theoretical results for the APA, we here consider the dependency between the weight-error vector and past noise in the mean-square analysis presented by Shin and Sayed in [“Mean-square performance of a family of affine projection algorithms,” IEEE Transactions on Signal Processing, vol. 52, no. 1, pp. 90–102, January 2004]. Through this work, we can also theoretically analyze the behavior of the periodic APA, which updates its weights periodically. Simulation results show that our theoretical results coincide closely with simulations. [ABSTRACT FROM AUTHOR]

The constant modulus algorithm based on fractional lower-order statistics (FLOS_CMA) has been proven to be an effective blind equalization method under α-stable noise. But there have been little results in the literature about the performance of this algorithm. In this paper, the steady-state mean-square error (MSE) performance of the FLOS_CMA is studied, and the approximate analytical expressions for real- and complex-valued data under stationary and non-stationary environments are derived, respectively, based on the energy-preserving relation and a Taylor series expansion. Based on the derived expression, an estimate for the FLOS_CMA step-size interval to ensure its convergence and stability is obtained, when it is initialized sufficiently close to the zero-forcing solution. Finally, simulation studies are undertaken to support the analysis. [ABSTRACT FROM AUTHOR]

Abstract: A novel self-adaptive differential evolution (SADE) algorithm is proposed in this paper. SADE adjusts the mutation rate F and the crossover rate adaptively, taking account of the different distribution of population. In order to balance an individual’s exploration and exploitation capability for different evolving phases, and are equal to two different self-adjusted nonlinear functions. Attention is concentrated on varying  and  dynamically with each generation evolution. SADE maintains the diversity of population and improves the global convergence ability. It also improves the efficiency and success rate and avoids the premature convergence. Simulation and comparisons based on test-sets of CSPs demonstrate the effectiveness, efficiency and robustness of the proposed algorithm. [Copyright &y& Elsevier]

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with repetitive control of Hamiltonian systems, which is based on iterative learning control utilizing the variational symmetry of those systems. Variational symmetry allows us to obtain an algorithm to solve a certain class of optimal control problems in a repetitive control framework. Therefore, the proposed method can deal with not only trajectory tracking control problems but also optimal trajectory generation problems, never before considered in a repetitive control framework. A convergence analysis of this algorithm is also discussed. Furthermore, some numerical simulations demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg–Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses a novel fully implicit formulation for a one-dimensional electrostatic particle-in-cell (PIC) plasma simulation approach. Unlike earlier implicit electrostatic PIC approaches (which are based on a linearized Vlasov–Poisson formulation), ours is based on a nonlinearly converged Vlasov–Ampére (VA) model. By iterating particles and fields to a tight nonlinear convergence tolerance, the approach features superior stability and accuracy properties, avoiding most of the accuracy pitfalls in earlier implicit PIC implementations. In particular, the formulation is stable against temporal (Courant–Friedrichs–Lewy) and spatial (aliasing) instabilities. It is charge- and energy-conserving to numerical round-off for arbitrary implicit time steps (unlike the earlier “energy-conserving” explicit PIC formulation, which only conserves energy in the limit of arbitrarily small time steps). While momentum is not exactly conserved, errors are kept small by an adaptive particle sub-stepping orbit integrator, which is instrumental to prevent particle tunneling (a deleterious effect for long-term accuracy). The VA model is orbit-averaged along particle orbits to enforce an energy conservation theorem with particle sub-stepping. As a result, very large time steps, constrained only by the dynamical time scale of interest, are possible without accuracy loss. Algorithmically, the approach features a Jacobian-free Newton–Krylov solver. A main development in this study is the nonlinear elimination of the new-time particle variables (positions and velocities). Such nonlinear elimination, which we term particle enslavement, results in a nonlinear formulation with memory requirements comparable to those of a fluid computation, and affords us substantial freedom in regards to the particle orbit integrator. Numerical examples are presented that demonstrate the advertised properties of the scheme. In particular, long-time ion acoustic wave simulations show that numerical accuracy does not degrade even with very large implicit time steps, and that significant CPU gains are possible. [Copyright &y& Elsevier]

nt colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms. [ABSTRACT FROM AUTHOR]

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton–Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface. [ABSTRACT FROM PUBLISHER]

In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased in proportion to its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without inducing oscillation. In the proposed method, because an immune feedback law is changing the learning rate of the neural networks individually and adaptively, it is expected that the neural cost function will reach its minimum rapidly, resulting in a reduced training time. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed to validate the convergence properties of the method. Control results show that the adaptive learning rate neural network control structure can outperform linear controllers and conventional neural network controllers for active random noise control. [ABSTRACT FROM AUTHOR]

In this paper, we present an extended Kalman filter (EKF)-based estimator for simultaneous localization and mapping (SLAM) with processing requirements that are linear in the number of features in the map. The proposed algorithm, called the Power-SLAM, is based on three key ideas. Firstly, by introducing the Global Map Postponement method, approximations necessary for ensuring linear computational complexity of EKF-based SLAM are delayed over multiple time steps. Then by employing the Power Method, only the most informative of the Kalman vectors, generated during the postponement phase, are retained for updating the covariance matrix. This ensures that the information loss during each approximation epoch is minimized. Next, linear-complexity, rank-2 updates, that minimize the trace of the covariance matrix, are employed to increase the speed of convergence of the estimator. The resulting estimator, in addition to being conservative as compared to the standard EKF, has processing requirements that can be adjusted depending on the availability of computational resources. Lastly, simulation and experimental results are presented that demonstrate the accuracy of the proposed algorithm (Power-SLAM) when compared to the standard EKF-based SLAM with quadratic computational cost and two linear-complexity competing alternatives. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, identification of structured nonlinear systems is considered. Using linear fractional transformations (LFT), the a priori information regarding the structural interconnection is systematically exploited. A parametric approach to the identification problem is investigated, where it is assumed that the linear part of the interconnection is given and the input to the nonlinear part is measurable. An algorithm for the identification of the nonlinear part is proposed. The uniqueness properties of the estimate provided by the algorithm are examined. It is shown that the estimate converges asymptotically to its true value under a certain persistence of excitation condition. Two simulated examples and a real-data example are presented to show the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle (aGB) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension. [Copyright &y& Elsevier]

In view of the support vectors of ε-SVR that are not distributed in the ε belt and only located on the outskirts of the ε belt, a novel algorithm to construct ε-SVR of a large-scale training sample set is proposed in this paper. It computes firstly the ε-SVR hyper-plane of a small training sample set and the distances d of all samples to the hyper-plane, then deletes the samples not in field ε ≤ d ≤ dmax and searches SVs gradually in the scope ε ≤ d ≤ dmax, and trains step-by-step the final ε-SVR. Finally, it analyzes the time complexity of the algorithm, and verifies its convergence in the theory and tests its efficiency by the simulation. [ABSTRACT FROM AUTHOR]

In this paper we study different algorithms for backward stochastic differential equations (BSDE in short) basing on random walk framework for 1-dimensional Brownian motion. Implicit and explicit schemes for both BSDE and reflected BSDE are introduced. Then we prove the convergence of different algorithms and present simulation results for different types of BSDEs. [ABSTRACT FROM AUTHOR]

Abstract: The design of the extreme-scale platforms that are expected to become available in the forthcoming decade will represent a convergence of technological trends and the boundary conditions imposed by over half a century of algorithm and application software development. These platforms will be hierarchical because they provide coarse grain parallelism between nodes and fine grain parallelism within each node. They are also expected to be very heterogeneous since multi-core chips and accelerators have completely different architectures and potentials. It is clear that such a degree of complexity will embody radical changes that will render obsolete the current software infrastructure for large-scale scientific applications. In this paper, we illustrate a hierarchical algorithmic approach for the implementation of an efficient parallel sparse linear solver that combines direct and iterative methods. Such a hybrid approach exploits the advantages of both numerical techniques and enables the use of several levels and grains of parallelism. This combination express different levels of parallelism and permits an optimal trade-off between numerical and parallel efficiency. Consequently, such a numerical technique appears as a promising candidate for intensive simulations on future many-core parallel platforms. [Copyright &y& Elsevier]

This paper presents a numerical method to simulate an incompressible fluid through an immersed porous interface. The interface is modeled by a surface measure term in the Navier-Stokes equations and it is characterized by a resistance parameter. This approach can be used, for example, to model valves or to simulate blood flood through an immersed stent. Starting from a monolithic formulation proposed recently, a fractional step algorithm is derived. The difficult point is that this formulation is singular when the resistance vanishes, which can be a serious issue in some applications. We show that an appropriate Nitsche treatment of the interface condition fixes this problem and ensures uniform energy stability in time for any nonnegative value of the resistance. The theoretical stability and convergence results are illustrated with numerical experiments. [ABSTRACT FROM AUTHOR]

Abstract: In system identification when a secondary path follows the adaptive filter, the FxLMS algorithm is usually applied for updating the adaptive filter. Although several FxLMS convergence analyses have been conducted in detail, only a few have intended to derive a convergence condition. In fact, available FxLMS convergence conditions are only accurate for simplified cases with pure delay secondary paths or multi-sinusoidal input signals. This paper studies the FxLMS convergence behavior for moving average secondary paths and stochastic input signals. A novel model for predicting the FxLMS convergence behavior is developed. Based on this model, a necessary and sufficient condition for the convergence of the FxLMS is derived. Also, the condition leading to the fastest convergence is proposed. Compared to previously derived convergence conditions, the proposed condition applies to more general secondary paths. Results obtained from this study are found to correspond very well to those obtained from simulation experiments. [Copyright &y& Elsevier]

Abstract: Solving a sparse system of linear equations is one of the most fundamental operations inside any circuit simulator. The equations/rows in the matrix are often rearranged/permuted before factorization and applying direct or iterative methods to obtain the solution. Permuting the rows of the matrix so that the entries with large absolute values lie on the diagonal has several advantages like better numerical stability for direct methods (e.g., Gaussian elimination) and faster convergence for indirect methods (such as the Jacobi method). Duff (2009) has formulated this as a weighted bipartite matching problem (the MC64 algorithm). In this paper we improve the performance of the MC64 algorithm with a new labeling technique which improves the asymptotic complexity of updating dual variables from to , where is the order of the matrix and is the number of non-zeros. Experimental results from using the new algorithm, when benchmarked with both industry benchmarks and UFL sparse matrix collection, are very promising. Our algorithm is more than 60 times faster (than Duff’s algorithm) for sparse matrices with at least a million non-zeros. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates model order reduction (MOR) techniques that can be used in conjunction with finite element schemes to generate computationally efficient solutions for multiphysics MEMS simulation. The Lanczos and Arnoldi algorithms are implemented to extract low dimensional Krylov subspaces from the finite element discretized system for model order reduction. A deflation procedure is employed in both algorithms to improve the solution convergence of the implicit iterations together with stopping criteria to automatically determine the reduced model size. Reduced order electrothermal–mechanical models are generated for a MEMS microgripper using the developed programs. A Guyan-based ANSYS substructuring analysis of the same device is also performed. Results discussion on all three techniques including preservation of full scale model properties such as dynamic behavior and stability are presented along with comparisons of reduced and full model simulation. The developed programs automatically generate compact structure preserving models and can be used to significantly improve the computational efficiency without much loss in accuracy and model stability for coupled-field MEMS simulation. [ABSTRACT FROM AUTHOR]