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Gradient Algorithm (gradient + algorithm)
Selected AbstractsAn MPI Parallel Implementation of Newmark's MethodCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2000Ali Namazifard The standard message-passing interface (MPI) is used to parallelize Newmark's method. The linear matrix equation encountered at each time step is solved using a preconditioned conjugate gradient algorithm. Data are distributed over the processors of a given parallel computer on a degree-of-freedom basis; this produces effective load balance between the processors and leads to a highly parallelized code. The portability of the implementation of this scheme is tested by solving some simple problems on two different machines: an SGI Origin2000 and an IBM SP2. The measured times demonstrate the efficiency of the approach and highlight the maintenance advantages that arise from using a standard parallel library such as MPI. [source] A stopping criterion for the conjugate gradient algorithm in the framework of anisotropic adaptive finite elementsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 4 2009M. Picasso Abstract We propose a simple stopping criterion for the conjugate gradient (CG) algorithm in the framework of anisotropic, adaptive finite elements for elliptic problems. The goal of the adaptive algorithm is to find a triangulation such that the estimated relative error is close to a given tolerance TOL. We propose to stop the CG algorithm whenever the residual vector has Euclidian norm less than a small fraction of the estimated error. This stopping criterion is based on a posteriori error estimates between the true solution u and the computed solution u (the superscript n stands for the CG iteration number, the subscript h for the typical mesh size) and on heuristics to relate the error between uh and u to the residual vector. Numerical experiments with anisotropic adaptive meshes show that the total number of CG iterations can be divided by 10 without significant discrepancy in the computed results. Copyright © 2008 John Wiley & Sons, Ltd. [source] A cascadic conjugate gradient algorithm for mass conservative, semi-implicit discretization of the shallow water equations on locally refined structured gridsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 1-2 2002Luca Bonaventura Abstract A semi-implicit, mass conservative discretization scheme is applied to the two-dimensional shallow water equations on a hierarchy of structured, locally refined Cartesian grids. Different resolution grids are fully interacting and the discrete Helmholtz equation obtained from the semi-implicit discretization is solved by the cascadic conjugate gradient method. A flux correction is applied at the interface between the coarser and finer discretization grids, so as to ensure discrete mass conservation, along with symmetry and diagonal dominance of the resulting matrix. Two-dimensional idealized simulations are presented, showing the accuracy and the efficiency of the resulting method. Copyright © 2002 John Wiley & Sons, Ltd. [source] Matching a system behavior with a known set of models: A quadratic optimization-based adaptive solutionINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 9 2009Moisés Bonilla Abstract The matching process between a time-domain external behavior of a lumped single-input single-output dynamical system and a known set of linear continuous time-invariant models is tackled in this paper. The proposed online solution is based on an adaptive structure detector, which in finite time locates in the known set of models the one corresponding to the observed external behavior; the detector results from the solution of a constrained quadratic optimization problem. The problem is expressed in terms of the time-domain activity of a family of discriminating filters and is solved via a normalized gradient algorithm, which avoids mismatching due to the presence of structural zeros in the filters and can take into account band-limited high-frequency measurement noise. A failure detection problem concerning a simulated servomechanism is included in order to illustrate the proposed solution. Copyright © 2008 John Wiley & Sons, Ltd. [source] Blind separation of convolutive mixtures of cyclostationary signalsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2004Wenwu Wang Abstract An adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary signals is proposed. The algorithm is derived by applying natural gradient iterative learning to a novel cost function which is defined according to the wide sense cyclostationarity of signals and can be deemed as a new member of the family of natural gradient algorithms for convolutive mixtures. A method based on estimating the cycle frequencies required for practical implementation of the proposed algorithm is presented. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures. Copyright © 2004 John Wiley & Sons, Ltd. [source] Natural gradient algorithm for neural networks applied to non-linear high power amplifiers,INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 8 2002H. Abdulkader Abstract This paper investigates the processing techniques for non-linear high power amplifiers (HPA) using neural networks (NNs). Several applications are presented: Identification and Predistortion of the HPA. Various Neural Network structures are proposed to identify and predistort the HPA. Since a few decades, NNs have shown excellent performance in solving complex problems (like classification, recognition, etc.) but usually they suffer from slow convergence speed. Here, we propose to use the natural gradient instead of the classical ordinary gradient in order to enhance the convergence properties. Results are presented concerning identification and predistortion using classical and natural gradient. Practical implementations issues are given at the end of the paper. Copyright © 2002 John Wiley & Sons, Ltd. [source] Neural network modeling of physical properties of chemical compoundsINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 1 2001J. Kozio Abstract Three different models relating structural descriptors to normal boiling points, melting points, and refractive indexes of organic compounds have been developed using artificial neural networks. A newly elaborated set of molecular descriptors was evaluated to determine their utility in quantitative structure,property relationship (QSPR) studies. Applying two data sets containing 190 amines and 393 amides, neural networks were trained to predict physical properties with close to experimental accuracy, using the conjugated gradient algorithm. Obtained results have shown a high predictive ability of learned neural networks models. The fit error for the predicted properties values compared to experimental data is relatively small. © 2001 John Wiley & Sons, Inc. Int J Quant Chem 84: 117,126, 2001 [source] A penalized likelihood approach to image warpingJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2001C. A. Glasbey A warping is a function that deforms images by mapping between image domains. The choice of function is formulated statistically as maximum penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The paper addresses two issues simultaneously, of how to choose the warping function and how to assess the alignment. A new, Fourier,von Mises image model is identified, with phase differences between Fourier-transformed images having von Mises distributions. Also, new, null set distortion criteria are proposed, with each criterion uniquely minimized by a particular set of polynomial functions. A conjugate gradient algorithm is used to estimate the warping function, which is numerically approximated by a piecewise bilinear function. The method is motivated by, and used to solve, three applied problems: to register a remotely sensed image with a map, to align microscope images obtained by using different optics and to discriminate between species of fish from photographic images. [source] An efficient gridding reconstruction method for multishot non-Cartesian imaging with correction of off-resonance artifactsMAGNETIC RESONANCE IN MEDICINE, Issue 6 2010Yuguang Meng Abstract An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the off-resonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multiple-shot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4.7 T. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source] Single-step nonlinear diffusion tensor estimation in the presence of microscopic and macroscopic motion,MAGNETIC RESONANCE IN MEDICINE, Issue 5 2008Murat Aksoy Abstract Patient motion can cause serious artifacts in diffusion tensor imaging (DTI), diminishing the reliability of the estimated diffusion tensor information. Studies in this field have so far been limited mainly to the correction of miniscule physiological motion. In order to correct for gross patient motion it is not sufficient to correct for misregistration between successive shots; the change in the diffusion-encoding direction must also be accounted for. This becomes particularly important for multishot sequences, whereby,in the presence of motion,each shot is encoded with a different diffusion weighting. In this study a general mathematical framework to correct for gross patient motion present in a multishot and multicoil DTI scan is presented. A signal model is presented that includes the effect of rotational and translational motion in the patient frame of reference. This model was used to create a nonlinear least-squares formulation, from which the diffusion tensors were obtained using a nonlinear conjugate gradient algorithm. Applications to both phantom simulations and in vivo studies showed that in the case of gross motion the proposed algorithm performs superiorly compared to conventional methods used for tensor estimation. Magn Reson Med 59:1138,1150, 2008. © 2008 Wiley-Liss, Inc. [source] Natural gradient-projection algorithm for distribution controlOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 5 2009Zhenning Zhang Abstract In this paper, we use an information geometric algorithm to solve the distribution control problem. Here, we consider the distribution of the output determined by the control input only. We set up two manifolds that are formed by the B-spline functions and the system output probability density functions, and we call them the B-spline manifold(B) and the system output manifold(M), respectively. Moreover, we call the new designed algorithm natural gradient-projection algorithm. In the natural gradient step, we use natural gradient algorithm to obtain an optimal trajectory of the weight vector on the B-spline manifold from the viewpoint of information geometry. In the projection step, we project the selected points on B onto M. The coordinates of the projections on M give the trajectory of the control input u. Copyright © 2008 John Wiley & Sons, Ltd. [source] A matrix gradient algorithm for identification of parameterized time-varying parametersASIAN JOURNAL OF CONTROL, Issue 1 2009Min-Shin Chen Abstract This paper considers the problem of estimating time-varying parameters which can be parameterized by a series of arbitrary known basis functions. It is shown that this problem is equivalent to the observer design problem for a "matrix" dynamic system. A "matrix" gradient algorithm, which mimics the well-known "vector" gradient algorithm, is proposed to estimate the unknown matrix. The contribution of this paper is to show that convergence of the proposed matrix algorithm is guaranteed by the persistent excitations of both the regressor and the basis functions. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] A GRADIENT BASED ADAPTIVE CONTROL ALGORITHM FOR DUAL-RATE SYSTEMSASIAN JOURNAL OF CONTROL, Issue 4 2006Feng Ding ABSTRACT In this paper, using a polynomial transformation technique, we derive a mathematical model for dual-rate systems. Based on this model, we use a stochastic gradient algorithm to estimate unknown parameters directly from the dual-rate input-output data, and then establish an adaptive control algorithm for dual-rate systems. We prove that the parameter estimation error converges to zero under persistent excitation, and the parameter estimation based control algorithm can achieve virtually asymptotically optimal control and ensure the closed-loop systems to be stable and globally convergent. The simulation results are included. [source] On Estimation and Prediction for Spatial Generalized Linear Mixed ModelsBIOMETRICS, Issue 1 2002Hao Zhang Summary. We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture. [source] | |||||||||||||||||||||||||