WorldWideScience

Sample records for adaptive general linear

  1. Adaptive quasi-likelihood estimate in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    CHEN Xia; CHEN Xiru

    2005-01-01

    This paper gives a thorough theoretical treatment on the adaptive quasilikelihood estimate of the parameters in the generalized linear models. The unknown covariance matrix of the response variable is estimated by the sample. It is shown that the adaptive estimator defined in this paper is asymptotically most efficient in the sense that it is asymptotic normal, and the covariance matrix of the limit distribution coincides with the one for the quasi-likelihood estimator for the case that the covariance matrix of the response variable is completely known.

  2. Adaptive Error Estimation in Linearized Ocean General Circulation Models

    Science.gov (United States)

    Chechelnitsky, Michael Y.

    1999-01-01

    Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large

  3. Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

    Science.gov (United States)

    Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John

    2015-01-01

    The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

  4. Biohybrid control of general linear systems using the adaptive filter model of cerebellum

    Directory of Open Access Journals (Sweden)

    Emma D. Wilson

    2015-07-01

    Full Text Available The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems such as the vestibulo-ocular reflex (VOR and to sensory processing problems such as the adaptive cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. In previous applications to inverse control problems the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity control of this plant results in unstable learning and control. To be more generally useful in engineering problems it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC scheme, which stabilises the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

  5. Generalized Linear Covariance Analysis

    Science.gov (United States)

    Carpenter, James R.; Markley, F. Landis

    2014-01-01

    This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

  6. Foundations of linear and generalized linear models

    CERN Document Server

    Agresti, Alan

    2015-01-01

    A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,

  7. Multivariate generalized linear mixed models using R

    CERN Document Server

    Berridge, Damon Mark

    2011-01-01

    Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

  8. Introduction to general and generalized linear models

    CERN Document Server

    Madsen, Henrik

    2010-01-01

    IntroductionExamples of types of data Motivating examples A first view on the modelsThe Likelihood PrincipleIntroduction Point estimation theory The likelihood function The score function The information matrix Alternative parameterizations of the likelihood The maximum likelihood estimate (MLE) Distribution of the ML estimator Generalized loss-function and deviance Quadratic approximation of the log-likelihood Likelihood ratio tests Successive testing in hypothesis chains Dealing with nuisance parameters General Linear ModelsIntroduction The multivariate normal distribution General linear mod

  9. Generalized, Linear, and Mixed Models

    CERN Document Server

    McCulloch, Charles E; Neuhaus, John M

    2011-01-01

    An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m

  10. Multivariate covariance generalized linear models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Jørgensen, Bent

    2016-01-01

    We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...... are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions...

  11. Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

    Directory of Open Access Journals (Sweden)

    Nauman Khalid Qureshi

    2017-07-01

    Full Text Available In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS signals utilizable in a two-class [motor imagery (MI and rest; mental rotation (MR and rest] brain–computer interface (BCI is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05. These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.

  12. Speaker Adaptation with Transformation Matrix Linear Interpolation

    Institute of Scientific and Technical Information of China (English)

    XU Xiang-hua; ZHU Jie

    2004-01-01

    A transformation matrix linear interpolation (TMLI) approach for speaker adaptation is proposed. TMLI uses the transformation matrixes produced by MLLR from selected training speakers and the testing speaker. With only 3 adaptation sentences, the performance shows a 12.12% word error rate reduction. As the number of adaptation sentences increases, the performance saturates quickly. To improve the behavior of TMLI for large amounts of adaptation data, the TMLI+MAP method which combines TMLI with MAP technique is proposed. Experimental results show TMLI+MAP achieved better recognition accuracy than MAP and MLLR+MAP for both small and large amounts of adaptation data.

  13. [General practice--linear thinking and complexity].

    Science.gov (United States)

    Stalder, H

    2006-09-27

    As physicians, we apply and teach linear thinking. This approach permits to dissect the patient's problem to the molecular level and has contributed enormously to the knowledge and progress of medicine. The linear approach is particularly useful in medical education, in quantitative research and helps to resolve simple problems. However, it risks to be rigid. Living beings (such as patients and physicians!) have to be considered as complex systems. A complex system cannot be dissected into its parts without losing its identity. It is dependent on its past and interactions with the outside are often followed by unpredictable reactions. The patient-centred approach in medicine permits the physician, a complex system himself, to integrate the patient's system and to adapt to his reality. It is particularly useful in general medicine.

  14. Linear zonal atmospheric prediction for adaptive optics

    Science.gov (United States)

    McGuire, Patrick C.; Rhoadarmer, Troy A.; Coy, Hanna A.; Angel, J. Roger P.; Lloyd-Hart, Michael

    2000-07-01

    We compare linear zonal predictors of atmospheric turbulence for adaptive optics. Zonal prediction has the possible advantage of being able to interpret and utilize wind-velocity information from the wavefront sensor better than modal prediction. For simulated open-loop atmospheric data for a 2- meter 16-subaperture AO telescope with 5 millisecond prediction and a lookback of 4 slope-vectors, we find that Widrow-Hoff Delta-Rule training of linear nets and Back- Propagation training of non-linear multilayer neural networks is quite slow, getting stuck on plateaus or in local minima. Recursive Least Squares training of linear predictors is two orders of magnitude faster and it also converges to the solution with global minimum error. We have successfully implemented Amari's Adaptive Natural Gradient Learning (ANGL) technique for a linear zonal predictor, which premultiplies the Delta-Rule gradients with a matrix that orthogonalizes the parameter space and speeds up the training by two orders of magnitude, like the Recursive Least Squares predictor. This shows that the simple Widrow-Hoff Delta-Rule's slow convergence is not a fluke. In the case of bright guidestars, the ANGL, RLS, and standard matrix-inversion least-squares (MILS) algorithms all converge to the same global minimum linear total phase error (approximately 0.18 rad2), which is only approximately 5% higher than the spatial phase error (approximately 0.17 rad2), and is approximately 33% lower than the total 'naive' phase error without prediction (approximately 0.27 rad2). ANGL can, in principle, also be extended to make non-linear neural network training feasible for these large networks, with the potential to lower the predictor error below the linear predictor error. We will soon scale our linear work to the approximately 108-subaperture MMT AO system, both with simulations and real wavefront sensor data from prime focus.

  15. Homology stability for the general linear group

    NARCIS (Netherlands)

    Maazen, Hendrik

    1979-01-01

    This thesis studies the homology stability problem for general linear groups over Euclidean rings and over subrings of the field of rational numbers. Affine linear groups, acting on affine space rather than linear space, are also considered. In order to get stability results one establishes that cer

  16. Homology stability for the general linear group

    NARCIS (Netherlands)

    Maazen, Hendrik

    1979-01-01

    This thesis studies the homology stability problem for general linear groups over Euclidean rings and over subrings of the field of rational numbers. Affine linear groups, acting on affine space rather than linear space, are also considered. In order to get stability results one establishes that

  17. Synaptic dynamics: linear model and adaptation algorithm.

    Science.gov (United States)

    Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W

    2014-08-01

    In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and

  18. Generalized Cross-Gramian for Linear Systems

    DEFF Research Database (Denmark)

    Shaker, Hamid Reza

    2012-01-01

    The cross-gramian is a well-known matrix with embedded controllability and observability information. The cross-gramian is related to the Hankel operator and the Hankel singular values of a linear square system and it has several interesting properties. These properties make the cross-gramian...... popular in several applications including model reduction, control configuration selection and sensitivity analysis. The ordinary cross-gramian which has been defined in the literature is the solution of a Sylvester equation. This Sylvester equation is not always solvable and therefore for some linear...... square symmetric systems, the ordinary cross-gramian does not exist. To cope with this problem, a new generalized cross-gramian is introduced in this paper. In contrast to the ordinary cross-gramian, the generalized cross-gramian can be easily obtained for general linear systems and therefore can be used...

  19. Linear generalized synchronization of chaotic systems with uncertain parameters

    Institute of Scientific and Technical Information of China (English)

    Jia Zhen

    2008-01-01

    A more general form of projective synchronization,so called linear generalized synchronization(LGS)is proposed,which includes the generalized projective synchronization(GPS)and the hybrid projective synchronization(HPS)as its special cases.Based on the adaptive technique and Lyapunov stability theory,a general method for achieving the LGS between two chaotic or hyperchaotic systems with uncertain parameters in any scaling matrix is presented.Some numerical simulations are provided to show the effectiveness and feasibility of the proposed synchronization method.

  20. General linear dynamics - quantum, classical or hybrid

    CERN Document Server

    Elze, H-T; Vallone, F

    2011-01-01

    We describe our recent proposal of a path integral formulation of classical Hamiltonian dynamics. Which leads us here to a new attempt at hybrid dynamics, which concerns the direct coupling of classical and quantum mechanical degrees of freedom. This is of practical as well as of foundational interest and no fully satisfactory solution of this problem has been established to date. Related aspects will be observed in a general linear ensemble theory, which comprises classical and quantum dynamics in the form of Liouville and von Neumann equations, respectively, as special cases. Considering the simplest object characterized by a two-dimensional state-space, we illustrate how quantum mechanics is special in several respects among possible linear generalizations.

  1. 简单分段线性混沌系统与SETMOS混沌系统的自适应广义同步%Adaptive generalized synchronization of simple piecewise linear chaotic system and SETMOS chaotic system

    Institute of Scientific and Technical Information of China (English)

    刘保军; 蔡理; 冯朝文

    2012-01-01

    研究了基于SETMOS构成的、参数未知的类双涡卷混沌系统与结构不同的简单分段线性混沌系统的广义自适应同步方法.通过分析混沌系统的特点和广义同步的定义,基于李雅普诺夫稳定性理论,提出了一种新颖的、结构简单的自适应控制器和参数更新律,来实现不同结构、驱动系统参数未知的混沌系统的广义同步.这种方法还可以应用于不同结构或相同结构的其他同步问题,如自适应广义反同步等,应用范围较广.仿真结果进一步证实了该方法的有效性和可行性.%The generalized synchronization of different structure chaotic systems based on SETMOS with unknown parameters,double-scroll-like chaotic system and simple piecewise linear chaotic system is investigated with respect to an assumed function. By analyzing characteristics of the chaotic systems and definition of the generalized synchronization,based on Lyapnuov stability theorem,novel and simple adaptive controllers and corresponding parameter update law are proposed for generalized synchronization of different chaotic systems with unknown parameters.Further,if the function is changed,the theory can also be applied for other synchronization for different structure chaotic systems,such as adaptive generalized anti-synchronization.Numerical simulation results are provided to show the effectiveness and feasibility of the proposed theory.

  2. GENERALIZED DERIVATIONS ON PARABOLIC SUBALGEBRAS OF GENERAL LINEAR LIE ALGEBRAS

    Institute of Scientific and Technical Information of China (English)

    陈正新

    2014-01-01

    Let P be a parabolic subalgebra of a general linear Lie algebra gl(n, F) over a field F, where n ≥ 3, F contains at least n different elements, and char(F) 6= 2. In this article, we prove that generalized derivations, quasiderivations, and product zero derivations of P coincide, and any generalized derivation of P is a sum of an inner derivation, a central quasiderivation, and a scalar multiplication map of P. We also show that any commuting automorphism of P is a central automorphism, and any commuting derivation of P is a central derivation.

  3. Using R In Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    Mihaela Covrig

    2015-09-01

    Full Text Available This paper aims to approach the estimation of generalized linear models (GLM on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative Binomial distribution. The paper will briefly present the GLM methodology for count data, while the practical part will revolve around estimating and comparing models in which the response variable shows the number of claims in a portfolio of automobile insurance policies.

  4. Multivariate Generalized Linear Mixed Models Using R

    CERN Document Server

    Berridge, Damon M

    2011-01-01

    To provide researchers with the ability to analyze large and complex data sets using robust models, this book presents a unified framework for a broad class of models that can be applied using a dedicated R package (Sabre). The first five chapters cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs). The next few chapters extend to multivariate GLMMs and the last chapters address more specialized topics, such as parallel computing for large-scale analyses. Each chapter includes many real-world examples implemented using Sabre as well as exercises and

  5. Generalized Adaptive Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  6. Indirect techniques for adaptive input-output linearization of non-linear systems

    Science.gov (United States)

    Teel, Andrew; Kadiyala, Raja; Kokotovic, Peter; Sastry, Shankar

    1991-01-01

    A technique of indirect adaptive control based on certainty equivalence for input output linearization of nonlinear systems is proven convergent. It does not suffer from the overparameterization drawbacks of the direct adaptive control techniques on the same plant. This paper also contains a semiindirect adaptive controller which has several attractive features of both the direct and indirect schemes.

  7. ADAPTIVE GENERALIZED PREDICTIVE CONTROL OF SWITCHED SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    WANG Yi-jing; WANG Long

    2005-01-01

    The problem of adaptive generalized predictive control which consists of output prediction errors for a class of switched systems is studied. The switching law is determined by the output predictive errors of a finite number of subsystems. For the single subsystem and multiple subsystems cases, it is proved that the given direct algorithm of generalized predictive control guarantees the global convergence of the system. This algorithm overcomes the inherent drawbacks of the slow convergence and large transient errors for the conventional adaptive control.

  8. Generalized Quadratic Linearization of Machine Models

    OpenAIRE

    Parvathy Ayalur Krishnamoorthy; Kamaraj Vijayarajan; Devanathan Rajagopalan

    2011-01-01

    In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome th...

  9. General practice--linear thinking and complexity

    National Research Council Canada - National Science Library

    Stalder, H

    2006-01-01

    As physicians, we apply and teach linear thinking. This approach permits to dissect the patient's problem to the molecular level and has contributed enormously to the knowledge and progress of medicine...

  10. A Note on the Identifiability of Generalized Linear Mixed Models

    DEFF Research Database (Denmark)

    Labouriau, Rodrigo

    2014-01-01

    I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity ...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization......I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...

  11. Construction of extended exponential general linear methods 524 ...

    African Journals Online (AJOL)

    Construction of extended exponential general linear methods 524 for solving semi-linear problems. ... Journal Home > Vol 13, No 2 (2014) > ... This paper introduces a new approach for constructing higher order of EEGLM which have become ...

  12. Penalized maximum likelihood estimation for generalized linear point processes

    OpenAIRE

    2010-01-01

    A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood. Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we...

  13. Abstract Acceleration of General Linear Loops

    OpenAIRE

    2014-01-01

    International audience; We present abstract acceleration techniques for computing loop invariants for numerical programs with linear assignments and conditionals. Whereas abstract interpretation techniques typically over-approximate the set of reachable states iteratively, abstract acceleration captures the effect of the loop with a single, non-iterative transfer function applied to the initial states at the loop head. In contrast to previous acceleration techniques, our approach applies to a...

  14. Rapid, generalized adaptation to asynchronous audiovisual speech.

    Science.gov (United States)

    Van der Burg, Erik; Goodbourn, Patrick T

    2015-04-01

    The brain is adaptive. The speed of propagation through air, and of low-level sensory processing, differs markedly between auditory and visual stimuli; yet the brain can adapt to compensate for the resulting cross-modal delays. Studies investigating temporal recalibration to audiovisual speech have used prolonged adaptation procedures, suggesting that adaptation is sluggish. Here, we show that adaptation to asynchronous audiovisual speech occurs rapidly. Participants viewed a brief clip of an actor pronouncing a single syllable. The voice was either advanced or delayed relative to the corresponding lip movements, and participants were asked to make a synchrony judgement. Although we did not use an explicit adaptation procedure, we demonstrate rapid recalibration based on a single audiovisual event. We find that the point of subjective simultaneity on each trial is highly contingent upon the modality order of the preceding trial. We find compelling evidence that rapid recalibration generalizes across different stimuli, and different actors. Finally, we demonstrate that rapid recalibration occurs even when auditory and visual events clearly belong to different actors. These results suggest that rapid temporal recalibration to audiovisual speech is primarily mediated by basic temporal factors, rather than higher-order factors such as perceived simultaneity and source identity.

  15. Self-characterization of linear and nonlinear adaptive optics systems

    Science.gov (United States)

    Hampton, Peter J.; Conan, Rodolphe; Keskin, Onur; Bradley, Colin; Agathoklis, Pan

    2008-01-01

    We present methods used to determine the linear or nonlinear static response and the linear dynamic response of an adaptive optics (AO) system. This AO system consists of a nonlinear microelectromechanical systems deformable mirror (DM), a linear tip-tilt mirror (TTM), a control computer, and a Shack-Hartmann wavefront sensor. The system is modeled using a single-input-single-output structure to determine the one-dimensional transfer function of the dynamic response of the chain of system hardware. An AO system has been shown to be able to characterize its own response without additional instrumentation. Experimentally determined models are given for a TTM and a DM.

  16. Discrete Time Optimal Adaptive Control for Linear Stochastic Systems

    Institute of Scientific and Technical Information of China (English)

    JIANG Rui; LUO Guiming

    2007-01-01

    The least-squares(LS)algorithm has been used for system modeling for a long time. Without any excitation conditions, only the convergence rate of the common LS algorithm can be obtained. This paper analyzed the weighted least-squares(WLS)algorithm and described the good properties of the WLS algorithm. The WLS algorithm was then used for daptive control of linear stochastic systems to show that the linear closed-loop system was globally stable and that the system identification was consistent. Compared to the past optimal adaptive controller,this controller does not impose restricted conditions on the coefficients of the system, such as knowing the first coefficient before the controller. Without any persistent excitation conditions, the analysis shows that, with the regulation of the adaptive control, the closed-loop system was globally stable and the adaptive controller converged to the one-step-ahead optimal controller in some sense.

  17. Coded Adaptive Linear Precoded Discrete Multitone Over PLC Channel

    CERN Document Server

    Muhammad, Fahad Syed; Hélard, Jean-François; Crussière, Matthieu

    2008-01-01

    Discrete multitone modulation (DMT) systems exploit the capabilities of orthogonal subcarriers to cope efficiently with narrowband interference, high frequency attenuations and multipath fadings with the help of simple equalization filters. Adaptive linear precoded discrete multitone (LP-DMT) system is based on classical DMT, combined with a linear precoding component. In this paper, we investigate the bit and energy allocation algorithm of an adaptive LP-DMT system taking into account the channel coding scheme. A coded adaptive LPDMT system is presented in the power line communication (PLC) context with a loading algorithm which accommodates the channel coding gains in bit and energy calculations. The performance of a concatenated channel coding scheme, consisting of an inner Wei's 4-dimensional 16-states trellis code and an outer Reed-Solomon code, in combination with the proposed algorithm is analyzed. Theoretical coding gains are derived and simulation results are presented for a fixed target bit error ra...

  18. Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

    Institute of Scientific and Technical Information of China (English)

    Sheng Chen

    2006-01-01

    Adaptive digital filtering has traditionally been developed based on the minimum mean square error (MMSE)criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate (MSER) criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sampleby-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.

  19. Generalized Ultrametric Semilattices of Linear Signals

    Science.gov (United States)

    2014-01-23

    ultrametric semilattice with a totally ordered distance set is isomorphic to a space of that kind. It follows that the formal definition of...from the National Science Foundation (NSF awards \\#0720882 ( CSR -EHS: PRET), \\#0931843 (CPS: Large: ActionWebs), and \\#1035672 (CPS: Medium: Timing...distance set is isomorphic to a space of that kind. It follows that the formal definition of generalized ultrametric semilattices with totally ordered

  20. QUADRATIC INVARIANTS AND SYMPLECTIC STRUCTURE OF GENERAL LINEAR METHODS

    Institute of Scientific and Technical Information of China (English)

    Ai-guo Xiao; Shou-fu Li; Min Yang

    2001-01-01

    In this paper, we present some invariants and conservation laws of general linear methods applied to differential equation systems. We show that the quadratic invariants and symplecticity of the systems can be extended to general linear methods by a tensor product, and show that general linear methods with the matrix M=0 inherit in an extended sense the quadratic invariants possessed by the differential equation systems being integrated and preserve in an extended sense the symplectic structure of the phase space in the integration of Hamiltonian systems. These unify and extend existing relevant results on Runge-Kutta methods, linear multistep methods and one-leg methods. Finally, as special cases of general linear methods, we examine multistep Runge-Kutta methods, one-leg methods and linear two-step methods in detail.

  1. Generalized Multicarrier CDMA: Unification and Linear Equalization

    Directory of Open Access Journals (Sweden)

    Wang Zhengdao

    2005-01-01

    Full Text Available Relying on block-symbol spreading and judicious design of user codes, this paper builds on the generalized multicarrier (GMC quasisynchronous CDMA system that is capable of multiuser interference (MUI elimination and intersymbol interference (ISI suppression with guaranteed symbol recovery, regardless of the wireless frequency-selective channels. GMC-CDMA affords an all-digital unifying framework, which encompasses single-carrier and several multicarrier (MC CDMA systems. Besides the unifying framework, it is shown that GMC-CDMA offers flexibility both in full load (maximum number of users allowed by the available bandwidth and in reduced load settings. A novel blind channel estimation algorithm is also derived. Analytical evaluation and simulations illustrate the superior error performance and flexibility of uncoded GMC-CDMA over competing MC-CDMA alternatives especially in the presence of uplink multipath channels.

  2. Adaptive feedback linearization applied to steering of ships

    Directory of Open Access Journals (Sweden)

    Thor I. Fossen

    1993-10-01

    Full Text Available This paper describes the application of feedback linearization to automatic steering of ships. The flexibility of the design procedure allows the autopilot to be optimized for both course-keeping and course-changing manoeuvres. Direct adaptive versions of both the course-keeping and turning controller are derived. The advantages of the adaptive controllers are improved performance and reduced fuel consumption. The application of nonlinear control theory also allows the designer in a systematic manner to compensate for nonlinearities in the control design.

  3. QUEST+: A general multidimensional Bayesian adaptive psychometric method.

    Science.gov (United States)

    Watson, Andrew B

    2017-03-01

    QUEST+ is a Bayesian adaptive psychometric testing method that allows an arbitrary number of stimulus dimensions, psychometric function parameters, and trial outcomes. It is a generalization and extension of the original QUEST procedure and incorporates many subsequent developments in the area of parametric adaptive testing. With a single procedure, it is possible to implement a wide variety of experimental designs, including conventional threshold measurement; measurement of psychometric function parameters, such as slope and lapse; estimation of the contrast sensitivity function; measurement of increment threshold functions; measurement of noise-masking functions; Thurstone scale estimation using pair comparisons; and categorical ratings on linear and circular stimulus dimensions. QUEST+ provides a general method to accelerate data collection in many areas of cognitive and perceptual science.

  4. Flexible Satellite Attitude Control via Adaptive Fuzzy Linearization

    Institute of Scientific and Technical Information of China (English)

    GUAN Ping; LIU Xiang-dong; CHEN Jia-bin; LIU Xiao-he

    2005-01-01

    The adaptive fuzzy control is combined with input-output linearization control to constitute the hybrid controller. The control method is then applied to the attitude maneuver control of the flexible satellite.The basic control structure is given. The rules of the controller parameter selection, which guarantee the attitude stabilization of the satellite with parameter uncertainties, have been analyzed. Simulation results show that the precise attitude control is accomplished in spite of the uncertainty in the system.

  5. Adaptive spectral identification techniques in presence of undetected non linearities

    CERN Document Server

    Cella, G; Guidi, G M

    2002-01-01

    The standard procedure for detection of gravitational wave coalescing binaries signals is based on Wiener filtering with an appropriate bank of template filters. This is the optimal procedure in the hypothesis of addictive Gaussian and stationary noise. We study the possibility of improving the detection efficiency with a class of adaptive spectral identification techniques, analyzing their effect in presence of non stationarities and undetected non linearities in the noise

  6. Robust Adaptive Control via Neural Linearization and Compensation

    Directory of Open Access Journals (Sweden)

    Roberto Carmona Rodríguez

    2012-01-01

    Full Text Available We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.

  7. Generalized Linear Models with Applications in Engineering and the Sciences

    CERN Document Server

    Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J

    2012-01-01

    Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma

  8. Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes

    CERN Document Server

    Bardet, Jean-Marc

    2010-01-01

    This paper is first devoted to study an adaptive wavelet based estimator of the long memory parameter for linear processes in a general semi-parametric frame. This is an extension of Bardet {\\it et al.} (2008) which only concerned Gaussian processes. Moreover, the definition of the long memory parameter estimator is modified and asymptotic results are improved even in the Gaussian case. Finally an adaptive goodness-of-fit test is also built and easy to be employed: it is a chi-square type test. Simulations confirm the interesting properties of consistency and robustness of the adaptive estimator and test.

  9. Asymptotic normality and strong consistency of maximum quasi-likelihood estimates in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    YIN; Changming; ZHAO; Lincheng; WEI; Chengdong

    2006-01-01

    In a generalized linear model with q × 1 responses, the bounded and fixed (or adaptive) p × q regressors Zi and the general link function, under the most general assumption on the minimum eigenvalue of ∑ni=1 ZiZ'i, the moment condition on responses as weak as possible and the other mild regular conditions, we prove that the maximum quasi-likelihood estimates for the regression parameter vector are asymptotically normal and strongly consistent.

  10. An adaptive genetic algorithm for solving bilevel linear programming problem

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

  11. Natural connections given by general linear and classical connections

    OpenAIRE

    Janyška, Josef

    2004-01-01

    We assume a vector bundle $p: E\\to M$ with a general linear connection $K$ and a classical linear connection $\\Lam$ on $M$. We prove that all classical linear connections on the total space $E$ naturally given by $(\\Lam, K)$ form a 15-parameter family. Further we prove that all connections on $J^1 E$ naturally given by $(\\Lam, K)$ form a 14-parameter family. Both families of connections are described geometrically.

  12. A New Method for Solving General Dual Fuzzy Linear Systems

    Directory of Open Access Journals (Sweden)

    M. Otadi

    2013-09-01

    Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived

  13. Testing Parametric versus Semiparametric Modelling in Generalized Linear Models

    NARCIS (Netherlands)

    Härdle, W.K.; Mammen, E.; Müller, M.D.

    1996-01-01

    We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e. m(

  14. Minimal solution of general dual fuzzy linear systems

    Energy Technology Data Exchange (ETDEWEB)

    Abbasbandy, S. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin 34194-288 (Iran, Islamic Republic of)], E-mail: abbasbandy@yahoo.com; Otadi, M.; Mosleh, M. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Islamic Azad University, Firuozkooh Branch, Firuozkooh (Iran, Islamic Republic of)

    2008-08-15

    Fuzzy linear systems of equations, play a major role in several applications in various area such as engineering, physics and economics. In this paper, we investigate the existence of a minimal solution of general dual fuzzy linear equation systems. Two necessary and sufficient conditions for the minimal solution existence are given. Also, some examples in engineering and economic are considered.

  15. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

  16. Linear generalized synchronization of continuous-time chaotic systems

    Energy Technology Data Exchange (ETDEWEB)

    Lu Junguo E-mail: jglu@sjtu.edu.cn; Xi Yugeng

    2003-08-01

    This paper develops a general approach for constructing a response system to implement linear generalized synchronization (GS) with the drive continuous-time chaotic system. Some sufficient conditions of global asymptotic linear GS between the drive and response continuous-time chaotic systems are attained from rigorously modern control theory. Finally, we take Chua's circuit as an example for illustration and verification.

  17. An adaptive linearizer for 16-QAM transmission over non-linear satellite channels

    Science.gov (United States)

    Shanmugan, K. Sam; Ruggles, M. J.

    An adaptive nonlinear equalization scheme that consists of a predistorter located at the transmitting earth station and a linear equalizer at the receiving earth station is described. Algorithms for automatically adjusting the predistorter and the linear equalizer are presented. The effectiveness of the scheme is evaluated using simulations. It is shown that the scheme improves the performance of a 16-queued access memory system operating over a typical satellite channel by minimizing degradations in the signal as it is transmitted over a band-limited satellite channel.

  18. A general and simple method for obtaining R2 from generalized linear mixed‐effects models

    National Research Council Canada - National Science Library

    Nakagawa, Shinichi; Schielzeth, Holger; O'Hara, Robert B

    2013-01-01

    The use of both linear and generalized linear mixed‐effects models ( LMM s and GLMM s) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution...

  19. Penalized maximum likelihood estimation for generalized linear point processes

    DEFF Research Database (Denmark)

    Hansen, Niels Richard

    2010-01-01

    A generalized linear point process is specified in terms of an intensity that depends upon a linear predictor process through a fixed non-linear function. We present a framework where the linear predictor is parametrized by a Banach space and give results on Gateaux differentiability of the log-likelihood....... Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we derive results on the representation of the penalized maximum likelihood estimator in a special case and the gradient...... of the negative log-likelihood in general. The latter is used to develop a descent algorithm in the Sobolev space. We conclude the paper by extensions to multivariate and additive model specifications. The methods are implemented in the R-package ppstat....

  20. Linearly and Quadratically Separable Classifiers Using Adaptive Approach

    Institute of Scientific and Technical Information of China (English)

    Mohamed Abdel-Kawy Mohamed Ali Soliman; Rasha M. Abo-Bakr

    2011-01-01

    This paper presents a fast adaptive iterative algorithm to solve linearly separable classification problems in Rn.In each iteration,a subset of the sampling data (n-points,where n is the number of features) is adaptively chosen and a hyperplane is constructed such that it separates the chosen n-points at a margin e and best classifies the remaining points.The classification problem is formulated and the details of the algorithm are presented.Further,the algorithm is extended to solving quadratically separable classification problems.The basic idea is based on mapping the physical space to another larger one where the problem becomes linearly separable.Numerical illustrations show that few iteration steps are sufficient for convergence when classes are linearly separable.For nonlinearly separable data,given a specified maximum number of iteration steps,the algorithm returns the best hyperplane that minimizes the number of misclassified points occurring through these steps.Comparisons with other machine learning algorithms on practical and benchmark datasets are also presented,showing the performance of the proposed algorithm.

  1. Noether's theory of generalized linear nonholonomic mechanical systems

    Institute of Scientific and Technical Information of China (English)

    Dong Wen-Shan; Huang Bao-Xin; Fang Jian-Hui

    2011-01-01

    By introducing the quasi-symmetry of the infinitesimal transformation of the transformation group Gr, the Noether's theorem and the Noether's inverse theorem for generalized linear nonholonomic mechanical systems are obtained in a generalized compound derivative space. An example is given to illustrate the application of the result.

  2. Controllability of Linear Systems on Generalized Heisenberg Groups

    OpenAIRE

    Dath, Mouhamadou; Jouan, Philippe

    2015-01-01

    This paper is devoted to the study of controllability of linear systems on generalized Heisenberg groups. Some general necessary controllability conditions and some sufficient ones are provided. We introduce the notion of decoupled systems, and more precise controllability criteria are stated for them.

  3. Adaptive Unified Biased Estimators of Parameters in Linear Model

    Institute of Scientific and Technical Information of China (English)

    Hu Yang; Li-xing Zhu

    2004-01-01

    To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.

  4. Adaptive distributed parameter and input estimation in linear parabolic PDEs

    KAUST Repository

    Mechhoud, Sarra

    2016-01-01

    In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.

  5. Adaptive Semi-linear Inversion of Strong Gravitational Lens Imaging

    CERN Document Server

    Nightingale, James

    2014-01-01

    We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model magnification. The distinguishing feature of adaptive SLI is that every pixelization is derived from a random initialization, ensuring that data discretization is performed in a completely different and unique way for every lens model parameter set. We compare standard SLI on a fixed source pixel grid with the new method and demonstrate the shortcomings of the former when modeling singular power law ellipsoid (SPLE) lens profiles. In particular, we demonstrate the superior reliability and efficiency of adaptive SLI which, by design, fixes the number of degrees of freedom (NDOF) of the optimization and thereby removes biases present with other methods that allow the NDOF to vary. In addition, we highlight the importanc...

  6. McDonald Generalized Linear Failure Rate Distribution

    Directory of Open Access Journals (Sweden)

    Ibrahim Elbatal

    2014-10-01

    Full Text Available We introduce in this paper a new six-parameters generalized version of the generalized linear failure rate (GLFR distribution which is called McDonald Generalized Linear failure rate (McGLFR distribution. The new distribution is quite flexible and can be used effectively in modeling survival data and reliability problems. It can have a constant, decreasing, increasing, and upside down bathtub-and bathtub shaped failure rate function depending on its parameters. It includes some well-known lifetime distributions as special sub-models. Some structural properties of the new distribution are studied. Moreover we discuss maximum likelihood estimation of the unknown parameters of the new model.

  7. General Linear Models: An Integrated Approach to Statistics

    Directory of Open Access Journals (Sweden)

    Andrew Faulkner

    2008-09-01

    Full Text Available Generally, in psychology, the various statistical analyses are taught independently from each other. As a consequence, students struggle to learn new statistical analyses, in contexts that differ from their textbooks. This paper gives a short introduction to the general linear model (GLM, in which it is showed that ANOVA (one-way, factorial, repeated measure and analysis of covariance is simply a multiple correlation/regression analysis (MCRA. Generalizations to other cases, such as multivariate and nonlinear analysis, are also discussed. It can easily be shown that every popular linear analysis can be derived from understanding MCRA.

  8. Invertible Linear Maps on the General Linear Lie Algebras Preserving Solvability

    Institute of Scientific and Technical Information of China (English)

    CHEN ZHENG-XIN; CHEN QIONG

    2012-01-01

    Let Mn be the algebra of all n × n complex matrices and gl(n,C) be the general linear Lie algebra,where n ≥ 2.An invertible linear map ?:gl(n,C) →gl(n,C) preserves solvability in both directions if both ? and ?-1 map every solvable Lie subalgebra of gl(n,C) to some solvable Lie subalgebra.In this paper we classify the invertible linear maps preserving solvability on gl(n,C) in both directions.As a sequence,such maps coincide with the invertible linear maps preserving commutativity on Mn in both directions.

  9. General Linear Models: An Integrated Approach to Statistics

    OpenAIRE

    Andrew Faulkner; Sylvain Chartier

    2008-01-01

    Generally, in psychology, the various statistical analyses are taught independently from each other. As a consequence, students struggle to learn new statistical analyses, in contexts that differ from their textbooks. This paper gives a short introduction to the general linear model (GLM), in which it is showed that ANOVA (one-way, factorial, repeated measure and analysis of covariance) is simply a multiple correlation/regression analysis (MCRA). Generalizations to other cases, such as multiv...

  10. Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

    CERN Document Server

    Faraway, Julian J

    2005-01-01

    Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

  11. Testing for one Generalized Linear Single Order Parameter

    DEFF Research Database (Denmark)

    Ellegaard, Niels Langager; Christensen, Tage Emil; Dyre, Jeppe

    work the order parameter may be chosen to have a non-exponential relaxation. The model predictions contradict the general consensus of the properties of viscous liquids in two ways: (i) The model predicts that following a linear isobaric temperature step, the normalized volume and entalpy relaxation...... functions are identical. This assumption conflicts with some (but not all) reports, utilizing the Tool-Narayanaswamy formalism to extrapolate from non-linear measurements to the linear regime. (ii) The model predicts that the theoretical "linear Prigogine-Defay" ratio is one. This ratio has never been...... responses or extrapolate from measurements of a glassy state away from equilibrium. Starting from a master equation description of inherent dynamics, we calculate the complex thermodynamic response functions. We device a way of testing for the generalized single order parameter model by measuring 3 complex...

  12. An Adaptive Non-Linear Map and Its Application

    Institute of Scientific and Technical Information of China (English)

    YAN Xuefeng

    2006-01-01

    A novel adaptive non-linear mapping (ANLM),integrating an adaptive mapping error (AME) with a chaosgenetic algorithm (CGA) including chaotic variable, was proposed to overcome the deficiencies of non-linear mapping (NLM). The value of AME weight factor is determined according to the relative deviation square of distance between the two mapping points and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. Due to chaotic mapping operator, the evolutional process of CGA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. The comparison results demonstrated that the whole performance of CGA is better than that of traditional genetic algorithm. Furthermore, a typical example of mapping eight-dimensional olive oil samples onto two-dimensional plane was employed to verify the effectiveness of ANLM. The results showed that the topology-preserving map obtained by ANLM can well represent the classification of original objects and is much better than that obtained by NLM.

  13. Estimation and variable selection for generalized additive partial linear models

    KAUST Repository

    Wang, Li

    2011-08-01

    We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.

  14. Dynamic generalized linear models for monitoring endemic diseases

    DEFF Research Database (Denmark)

    Lopes Antunes, Ana Carolina; Jensen, Dan Børge; Halasa, T.

    The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...... in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance...

  15. A Matrix Approach for General Higher Order Linear Recurrences

    Science.gov (United States)

    2011-01-01

    properties of linear recurrences (such as the well-known Fibonacci and Pell sequences ). In [2], Er defined k linear recurring sequences of order at...the nth term of the ith generalized order-k Fibonacci sequence . Communicated by Lee See Keong. Received: March 26, 2009; Revised: August 28, 2009...6], the author gave the generalized order-k Fibonacci and Pell (F-P) sequence as follows: For m ≥ 0, n > 0 and 1 ≤ i ≤ k uin = 2 muin−1 + u i n−2

  16. The Optimal Linear Combination of Multiple Predictors Under the Generalized Linear Models.

    Science.gov (United States)

    Jin, Hua; Lu, Ying

    2009-11-15

    Multiple alternative diagnostic tests for one disease are commonly available to clinicians. It's important to use all the good diagnostic predictors simultaneously to establish a new predictor with higher statistical utility. Under the generalized linear model for binary outcomes, the linear combination of multiple predictors in the link function is proved optimal in the sense that the area under the receiver operating characteristic (ROC) curve of this combination is the largest among all possible linear combination. The result was applied to analysis of the data from the Study of Osteoporotic Fractures (SOF) with comparison to Su and Liu's approach.

  17. The linear model and hypothesis a general unifying theory

    CERN Document Server

    Seber, George

    2015-01-01

    This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involve matrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality in the analysis of variance to other models, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.

  18. Thurstonian models for sensory discrimination tests as generalized linear models

    DEFF Research Database (Denmark)

    Brockhoff, Per B.; Christensen, Rune Haubo Bojesen

    2010-01-01

    Sensory discrimination tests such as the triangle, duo-trio, 2-AFC and 3-AFC tests produce binary data and the Thurstonian decision rule links the underlying sensory difference 6 to the observed number of correct responses. In this paper it is shown how each of these four situations can be viewed...... as a so-called generalized linear model. The underlying sensory difference 6 becomes directly a parameter of the statistical model and the estimate d' and it's standard error becomes the "usual" output of the statistical analysis. The d' for the monadic A-NOT A method is shown to appear as a standard...... linear contrast in a generalized linear model using the probit link function. All methods developed in the paper are implemented in our free R-package sensR (http://www.cran.r-project.org/package=sensR/). This includes the basic power and sample size calculations for these four discrimination tests...

  19. A random effects generalized linear model for reliability compositive evaluation

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    This paper first proposes a random effects generalized linear model to evaluate the storage life of one kind of high reliable and small sample-sized products by combining multi-sources information of products coming from the same population but stored at different environments. The relevant algorithms are also provided. Simulation results manifest the soundness and effectiveness of the proposed model.

  20. RF Circuit linearity optimization using a general weak nonlinearity model

    NARCIS (Netherlands)

    Cheng, W.; Oude Alink, M.S.; Annema, Anne J.; Croon, Jeroen A.; Nauta, Bram

    2012-01-01

    This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC

  1. The General Linear Model as Structural Equation Modeling

    Science.gov (United States)

    Graham, James M.

    2008-01-01

    Statistical procedures based on the general linear model (GLM) share much in common with one another, both conceptually and practically. The use of structural equation modeling path diagrams as tools for teaching the GLM as a body of connected statistical procedures is presented. A heuristic data set is used to demonstrate a variety of univariate…

  2. Applying the General Linear Model to Repeated Measures Problems.

    Science.gov (United States)

    Pohlmann, John T.; McShane, Michael G.

    The purpose of this paper is to demonstrate the use of the general linear model (GLM) in problems with repeated measures on a dependent variable. Such problems include pretest-posttest designs, multitrial designs, and groups by trials designs. For each of these designs, a GLM analysis is demonstrated wherein full models are formed and restrictions…

  3. A random effects generalized linear model for reliability compositive evaluation

    Institute of Scientific and Technical Information of China (English)

    ZHAO Hui; YU Dan

    2009-01-01

    This paper first proposes a random effects generalized linear model to evaluate the storage life of one kind of high reliable and small sample-sized products by combining multi-sources information of products coming from the same population but stored at different environments.The relevant algorithms are also provided.Simulation results manifest the soundness and effectiveness of the proposed model.

  4. Adaptive discontinuous Galerkin methods for non-linear reactive flows

    CERN Document Server

    Uzunca, Murat

    2016-01-01

    The focus of this monograph is the development of space-time adaptive methods to solve the convection/reaction dominated non-stationary semi-linear advection diffusion reaction (ADR) equations with internal/boundary layers in an accurate and efficient way. After introducing the ADR equations and discontinuous Galerkin discretization, robust residual-based a posteriori error estimators in space and time are derived. The elliptic reconstruction technique is then utilized to derive the a posteriori error bounds for the fully discrete system and to obtain optimal orders of convergence. As coupled surface and subsurface flow over large space and time scales is described by (ADR) equation the methods described in this book are of high importance in many areas of Geosciences including oil and gas recovery, groundwater contamination and sustainable use of groundwater resources, storing greenhouse gases or radioactive waste in the subsurface.

  5. Generalizing a categorization of students' interpretations of linear kinematics graphs

    Science.gov (United States)

    Bollen, Laurens; De Cock, Mieke; Zuza, Kristina; Guisasola, Jenaro; van Kampen, Paul

    2016-06-01

    We have investigated whether and how a categorization of responses to questions on linear distance-time graphs, based on a study of Irish students enrolled in an algebra-based course, could be adopted and adapted to responses from students enrolled in calculus-based physics courses at universities in Flanders, Belgium (KU Leuven) and the Basque Country, Spain (University of the Basque Country). We discuss how we adapted the categorization to accommodate a much more diverse student cohort and explain how the prior knowledge of students may account for many differences in the prevalence of approaches and success rates. Although calculus-based physics students make fewer mistakes than algebra-based physics students, they encounter similar difficulties that are often related to incorrectly dividing two coordinates. We verified that a qualitative understanding of kinematics is an important but not sufficient condition for students to determine a correct value for the speed. When comparing responses to questions on linear distance-time graphs with responses to isomorphic questions on linear water level versus time graphs, we observed that the context of a question influences the approach students use. Neither qualitative understanding nor an ability to find the slope of a context-free graph proved to be a reliable predictor for the approach students use when they determine the instantaneous speed.

  6. Model Based Adaptive Piecewise Linear Controller for Complicated Control Systems

    Directory of Open Access Journals (Sweden)

    Tain-Sou Tsay

    2014-01-01

    Full Text Available A model based adaptive piecewise linear control scheme for industry processes with specifications on peak overshoots and rise times is proposed. It is a gain stabilized control technique. Large gain is used for large tracking error to get fast response. Small gain is used between large and small tracking error for good performance. Large gain is used again for small tracking error to cope with large disturbance. Parameters of the three-segment piecewise linear controller are found by an automatic regulating time series which is function of output characteristics of the plant and reference model. The time series will be converged to steady values after the time response of the considered system matching that of the reference model. The proposed control scheme is applied to four numerical examples which have been compensated by PID controllers. Parameters of PID controllers are found by optimization method. It gives an almost command independent response and gives significant improvements for response time and performance.

  7. A new heuristic algorithm for general integer linear programming problems

    Institute of Scientific and Technical Information of China (English)

    GAO Pei-wang; CAI Ying

    2006-01-01

    A new heuristic algorithm is proposed for solving general integer linear programming problems.In the algorithm,the objective function hyperplane is used as a cutting plane,and then by introducing a special set of assistant sets,an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane.A simple numerical example shows that the algorithm is efficient for some problems,and therefore,of practical interest.

  8. Regularization Paths for Generalized Linear Models via Coordinate Descent

    Directory of Open Access Journals (Sweden)

    Jerome Friedman

    2010-02-01

    Full Text Available We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso, ℓ2 (ridge regression and mixtures of the two (the elastic net. The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.

  9. An adaptive feedback linearization strategy for variable speed wind energy conversion systems

    Energy Technology Data Exchange (ETDEWEB)

    Valenciaga, F.; Puleston, P.F.; Battaiotto, P.E.; Mantz, R.J. [Universidad Nacional de La Plata, Depto. de Electrotecnia, La Plata (Argentina)

    2000-07-01

    This paper presents a control strategy based on adaptive feedback linearization intended for variable speed grid-connected wind energy conversion systems (WECS). The proposed adaptive control law accomplishes energy capture maximization by tracking the wind speed fluctuations. In addition, it linearizes the system even in the presence of turbine model uncertainties, allowing the closed-loop dynamic behaviour to be determined by a simple tuning of the controller parameters. Particularly, the attention is focused on WECS with slip power recovery, which use a power conversion stage as a rotor-controlled double-output induction generator. However, the concepts behind the proposed control strategy are general and can be easily extended to other WECS configurations. (Author)

  10. Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation

    CERN Document Server

    Lin, Zhouchen; Su, Zhixun

    2011-01-01

    Low-rank representation (LRR) is an effective method for subspace clustering and has found wide applications in computer vision and machine learning. The existing LRR solver is based on the alternating direction method (ADM). It suffers from $O(n^3)$ computation complexity due to the matrix-matrix multiplications and matrix inversions, even if partial SVD is used. Moreover, introducing auxiliary variables also slows down the convergence. Such a heavy computation load prevents LRR from large scale applications. In this paper, we generalize ADM by linearizing the quadratic penalty term and allowing the penalty to change adaptively. We also propose a novel rule to update the penalty such that the convergence is fast. With our linearized ADM with adaptive penalty (LADMAP) method, it is unnecessary to introduce auxiliary variables and invert matrices. The matrix-matrix multiplications are further alleviated by using the skinny SVD representation technique. As a result, we arrive at an algorithm for LRR with comple...

  11. Generalized non-linear strength theory and transformed stress space

    Institute of Scientific and Technical Information of China (English)

    YAO Yangping; LU Dechun; ZHOU Annan; ZOU Bo

    2004-01-01

    Based on the test data of frictional materials and previous research achievements in this field, a generalized non-linear strength theory (GNST) is proposed. It describes non-linear strength properties on the π-plane and the meridian plane using a unified formula, and it includes almost all the present non-linear strength theories, which can be used in just one material. The shape of failure function of the GNST is a smooth curve between the SMP criterion and the Mises criterion on the π-plane, and an exponential curve on the meridian plane. Through the transformed stress space based on the GNST, the combination of the GNST and various constitutive models using p and q as stress parameters can be realized simply and rationally in three-dimensional stress state.

  12. General expression for linear and nonlinear time series models

    Institute of Scientific and Technical Information of China (English)

    Ren HUANG; Feiyun XU; Ruwen CHEN

    2009-01-01

    The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.

  13. Computation of Optimal Monotonicity Preserving General Linear Methods

    KAUST Repository

    Ketcheson, David I.

    2009-07-01

    Monotonicity preserving numerical methods for ordinary differential equations prevent the growth of propagated errors and preserve convex boundedness properties of the solution. We formulate the problem of finding optimal monotonicity preserving general linear methods for linear autonomous equations, and propose an efficient algorithm for its solution. This algorithm reliably finds optimal methods even among classes involving very high order accuracy and that use many steps and/or stages. The optimality of some recently proposed methods is verified, and many more efficient methods are found. We use similar algorithms to find optimal strong stability preserving linear multistep methods of both explicit and implicit type, including methods for hyperbolic PDEs that use downwind-biased operators.

  14. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina

    2012-08-03

    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  15. Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

    CERN Document Server

    Gottwald, Fabian; Ivanov, Sergei D; Kühn, Oliver

    2015-01-01

    Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation (GLE), which can be rigorously derived by means of a linear projection (LP) technique. Within this framework a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here we discuss that this task is most naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importa...

  16. Practical likelihood analysis for spatial generalized linear mixed models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Ribeiro, Paulo Justiniano

    2016-01-01

    We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are, respectiv......We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

  17. Generalized pattern search algorithms with adaptive precision function evaluations

    Energy Technology Data Exchange (ETDEWEB)

    Polak, Elijah; Wetter, Michael

    2003-05-14

    In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to a form that uses adaptive precision approximations to the cost function. These numerical approximations need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. Such an approach leads to substantial time savings in minimizing computationally expensive functions.

  18. Application of Bounded Linear Stability Analysis Method for Metrics-Driven Adaptive Control

    Science.gov (United States)

    Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje

    2009-01-01

    This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.

  19. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....

  20. Conditional likelihood inference in generalized linear mixed models.

    OpenAIRE

    Sartori, Nicola; Severini , T.A

    2002-01-01

    Consider a generalized linear model with a canonical link function, containing both fixed and random effects. In this paper, we consider inference about the fixed effects based on a conditional likelihood function. It is shown that this conditional likelihood function is valid for any distribution of the random effects and, hence, the resulting inferences about the fixed effects are insensitive to misspecification of the random effects distribution. Inferences based on the conditional likelih...

  1. A general theory of linear cosmological perturbations: bimetric theories

    CERN Document Server

    Lagos, Macarena

    2016-01-01

    We implement the method developed in [1] to construct the most general parametrised action for linear cosmological perturbations of bimetric theories of gravity. Specifically, we consider perturbations around a homogeneous and isotropic background, and identify the complete form of the action invariant under diffeomorphism transformations, as well as the number of free parameters characterising this cosmological class of theories. We discuss, in detail, the case without derivative interactions, and compare our results with those found in massive bigravity.

  2. On the unitarity of linearized General Relativity coupled to matter

    CERN Document Server

    Atkins, Michael

    2010-01-01

    We consider the unitarity of the S-matrix for linearized General Relativity coupled to particle physics models. Taking renormalization group effects of the Planck mass into account, we find that the scale at which unitarity is violated is strongly dependent on the particle content of the theory. We find that the requirement that the S-matrix be unitary up to the scale at which quantum gravitational effects become strong implies a bound on the particle content of the model.

  3. Credibility analysis of risk classes by generalized linear model

    Science.gov (United States)

    Erdemir, Ovgucan Karadag; Sucu, Meral

    2016-06-01

    In this paper generalized linear model (GLM) and credibility theory which are frequently used in nonlife insurance pricing are combined for reliability analysis. Using full credibility standard, GLM is associated with limited fluctuation credibility approach. Comparison criteria such as asymptotic variance and credibility probability are used to analyze the credibility of risk classes. An application is performed by using one-year claim frequency data of a Turkish insurance company and results of credible risk classes are interpreted.

  4. Electromagnetic axial anomaly in a generalized linear sigma model

    Science.gov (United States)

    Fariborz, Amir H.; Jora, Renata

    2017-06-01

    We construct the electromagnetic anomaly effective term for a generalized linear sigma model with two chiral nonets, one with a quark-antiquark structure, the other one with a four-quark content. We compute in the leading order of this framework the decays into two photons of six pseudoscalars: π0(137 ), π0(1300 ), η (547 ), η (958 ), η (1295 ) and η (1760 ). Our results agree well with the available experimental data.

  5. Estimation linear model using block generalized inverse of a matrix

    OpenAIRE

    Jasińska, Elżbieta; Preweda, Edward

    2013-01-01

    The work shows the principle of generalized linear model, point estimation, which can be used as a basis for determining the status of movements and deformations of engineering objects. The structural model can be put on any boundary conditions, for example, to ensure the continuity of the deformations. Estimation by the method of least squares was carried out taking into account the terms and conditions of the Gauss- Markov for quadratic forms stored using Lagrange function. The original sol...

  6. Residuals analysis of the generalized linear models for longitudinal data.

    Science.gov (United States)

    Chang, Y C

    2000-05-30

    The generalized estimation equation (GEE) method, one of the generalized linear models for longitudinal data, has been used widely in medical research. However, the related sensitivity analysis problem has not been explored intensively. One of the possible reasons for this was due to the correlated structure within the same subject. We showed that the conventional residuals plots for model diagnosis in longitudinal data could mislead a researcher into trusting the fitted model. A non-parametric method, named the Wald-Wolfowitz run test, was proposed to check the residuals plots both quantitatively and graphically. The rationale proposedin this paper is well illustrated with two real clinical studies in Taiwan.

  7. Non-linear, adaptive array processing for acoustic interference suppression.

    Science.gov (United States)

    Hoppe, Elizabeth; Roan, Michael

    2009-06-01

    A method is introduced where blind source separation of acoustical sources is combined with spatial processing to remove non-Gaussian, broadband interferers from space-time displays such as bearing track recorder displays. This differs from most standard techniques such as generalized sidelobe cancellers in that the separation of signals is not done spatially. The algorithm performance is compared to adaptive beamforming techniques such as minimum variance distortionless response beamforming. Simulations and experiments using two acoustic sources were used to verify the performance of the algorithm. Simulations were also used to determine the effectiveness of the algorithm under various signal to interference, signal to noise, and array geometry conditions. A voice activity detection algorithm was used to benchmark the performance of the source isolation.

  8. Local Linear Embedding Algorithm with Adaptively Determining Neighborhood

    Directory of Open Access Journals (Sweden)

    Zhenduo Wang

    2014-06-01

    Full Text Available Local linear embedding is a kind of very competitive nonlinear dimensionality reduction technique with good representational capacity for a broader range of manifolds and high computational efficiency. However, it is based on the assumption that the whole data manifolds are evenly distributed so that it determines the neighborhood for all points with the same neighborhood size. Accordingly, it fails to nicely deal with most real problems that are unevenly distributed. This paper presents a new approach that takes the general conceptual framework of Hessian locally linear embedding so as to guarantee its correctness in the setting of local isometry for an open connected subset, but dynamically determines the local neighborhood size for each point. This approach estimates the approximate geodesic distance between any two points by the shortest path in the local neighborhood graph, and then determines the neighborhood size for each point by using the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. This approach has clear geometry intuition as well as the better performance and stability. It deals with the sparsely sampled or noise contaminated data sets that are often unevenly distributed. The conducted experiments on benchmark data sets validate the proposed approach

  9. Comparative Study of Algorithms for Automated Generalization of Linear Objects

    Science.gov (United States)

    Azimjon, S.; Gupta, P. K.; Sukhmani, R. S. G. S.

    2014-11-01

    Automated generalization, rooted from conventional cartography, has become an increasing concern in both geographic information system (GIS) and mapping fields. All geographic phenomenon and the processes are bound to the scale, as it is impossible for human being to observe the Earth and the processes in it without decreasing its scale. To get optimal results, cartographers and map-making agencies develop set of rules and constraints, however these rules are under consideration and topic for many researches up until recent days. Reducing map generating time and giving objectivity is possible by developing automated map generalization algorithms (McMaster and Shea, 1988). Modification of the scale traditionally is a manual process, which requires knowledge of the expert cartographer, and it depends on the experience of the user, which makes the process very subjective as every user may generate different map with same requirements. However, automating generalization based on the cartographic rules and constrains can give consistent result. Also, developing automated system for map generation is the demand of this rapid changing world. The research that we have conveyed considers only generalization of the roads, as it is one of the indispensable parts of a map. Dehradun city, Uttarakhand state of India was selected as a study area. The study carried out comparative study of the generalization software sets, operations and algorithms available currently, also considers advantages and drawbacks of the existing software used worldwide. Research concludes with the development of road network generalization tool and with the final generalized road map of the study area, which explores the use of open source python programming language and attempts to compare different road network generalization algorithms. Thus, the paper discusses the alternative solutions for automated generalization of linear objects using GIS-technologies. Research made on automated of road network

  10. Generalized PID observer design for descriptor linear systems.

    Science.gov (United States)

    Wu, Ai-Guo; Duan, Guang-Ren; Fu, Yan-Ming

    2007-10-01

    A type of generalized proportional-integral-derivative observers is proposed for descriptor linear systems. Based on a general parametric solution to a type of generalized Sylvester matrix equations, a parametric design approach for such observers is established. The proposed approach provides parameterizations for all the observer gain matrices, gives the parametric expression for the corresponding left eigenvector matrix of the observer system matrix, realizes the elimination of impulsive behaviors, and guarantees the regularity of the observer system. The design method can offer all the degrees of design freedom, which can be utilized to achieve various desired system specifications and performances. In addition, a numerical example is employed to show the design procedure and illustrate the effect of the presented approach.

  11. General linear matrix model, Minkowski spacetime and the Standard Model

    CERN Document Server

    Belyea, Chris

    2010-01-01

    The Hermitian matrix model with general linear symmetry is argued to decouple into a finite unitary matrix model that contains metastable multidimensional lattice configurations and a fermion determinant. The simplest metastable state is a Hermitian Weyl kinetic operator of either handedness on a 3+1 D lattice with general nonlocal interactions. The Hermiticity produces 16 effective Weyl fermions by species doubling, 8 left- and 8 right-handed. These are identified with a Standard Model generation. Only local non-anomalous gauge fields within the soup of general fluctuations can survive at long distances, and the degrees of freedom for gauge fields of an $SU(8)_L X SU(8)_R$ GUT are present. Standard Model gauge symmetries associate with particular species symmetries, for example change of QCD color associates with permutation of doubling status amongst space directions. Vierbein gravity is probably also generated. While fundamental Higgs fields are not possible, low fermion current masses can arise from chira...

  12. Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

    Science.gov (United States)

    Gottwald, Fabian; Karsten, Sven; Ivanov, Sergei D.; Kühn, Oliver

    2015-06-01

    Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.

  13. Parametrizing linear generalized Langevin dynamics from explicit molecular dynamics simulations

    Energy Technology Data Exchange (ETDEWEB)

    Gottwald, Fabian; Karsten, Sven; Ivanov, Sergei D., E-mail: sergei.ivanov@uni-rostock.de; Kühn, Oliver [Institute of Physics, Rostock University, Universitätsplatz 3, 18055 Rostock (Germany)

    2015-06-28

    Fundamental understanding of complex dynamics in many-particle systems on the atomistic level is of utmost importance. Often the systems of interest are of macroscopic size but can be partitioned into a few important degrees of freedom which are treated most accurately and others which constitute a thermal bath. Particular attention in this respect attracts the linear generalized Langevin equation, which can be rigorously derived by means of a linear projection technique. Within this framework, a complicated interaction with the bath can be reduced to a single memory kernel. This memory kernel in turn is parametrized for a particular system studied, usually by means of time-domain methods based on explicit molecular dynamics data. Here, we discuss that this task is more naturally achieved in frequency domain and develop a Fourier-based parametrization method that outperforms its time-domain analogues. Very surprisingly, the widely used rigid bond method turns out to be inappropriate in general. Importantly, we show that the rigid bond approach leads to a systematic overestimation of relaxation times, unless the system under study consists of a harmonic bath bi-linearly coupled to the relevant degrees of freedom.

  14. Adaptive set-point tracking of the Lorenz chaotic system using non-linear feedback

    Energy Technology Data Exchange (ETDEWEB)

    Haghighatdar, F. [Department of Electronic Engineering, University of Isfahan, Hezar-Jerib St., Postal code: 8174673441, Isfahan (Iran, Islamic Republic of)], E-mail: fr_haghighat@yahoo.com; Ataei, M. [Department of Electronic Engineering, University of Isfahan, Hezar-Jerib St., Postal code: 8174673441, Isfahan (Iran, Islamic Republic of)], E-mail: mataei1971@yahoo.com

    2009-05-30

    In this paper, an adaptive control method for set-point tracking of the Lorenz chaotic system by using non-linear feedback is proposed. The design procedure of the proposed controller is accomplished in two steps. At the first step, using Lyapunov's direct method, a non-linear state feedback is selected so that without any need to apply identification techniques, in despite of the uncertain parameters existence in the system state equations, the asymptotic stability of the general Lorenz system is guaranteed in a stochastic point of the manifold containing general system equilibrium points. At the second step, a linear state feedback with adaptive gain is added to the prior controller to eliminate the tracking error. In order to guarantee the system asymptotic stability at desired set-point, the indirect Lyapunov's method is used. Finally, to show the effectiveness of the proposed methodology, the simulation results of different experiments including system parameters changes and set-point variation are provided.

  15. Confidence Intervals of Variance Functions in Generalized Linear Model

    Institute of Scientific and Technical Information of China (English)

    Yong Zhou; Dao-ji Li

    2006-01-01

    In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models (GLMs), when designs are fixed points and random variables respectively. Bias-corrected confidence bands are proposed for the (conditional) variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the (conditional) variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparametric autoregressive times series model with heteroscedastic conditional variance.

  16. Widely Linear Blind Adaptive Equalization for Transmitter IQ-Imbalance/Skew Compensation in Multicarrier Systems

    DEFF Research Database (Denmark)

    Porto da Silva, Edson; Zibar, Darko

    2016-01-01

    Simple analytical widely linear complex-valued models for IQ-imbalance and IQ-skew effects in multicarrier transmitters are presented. To compensate for such effects, a 4×4 MIMO widely linear adaptive equalizer is proposed and experimentally validated.......Simple analytical widely linear complex-valued models for IQ-imbalance and IQ-skew effects in multicarrier transmitters are presented. To compensate for such effects, a 4×4 MIMO widely linear adaptive equalizer is proposed and experimentally validated....

  17. A Graphical User Interface to Generalized Linear Models in MATLAB

    Directory of Open Access Journals (Sweden)

    Peter Dunn

    1999-07-01

    Full Text Available Generalized linear models unite a wide variety of statistical models in a common theoretical framework. This paper discusses GLMLAB-software that enables such models to be fitted in the popular mathematical package MATLAB. It provides a graphical user interface to the powerful MATLAB computational engine to produce a program that is easy to use but with many features, including offsets, prior weights and user-defined distributions and link functions. MATLAB's graphical capacities are also utilized in providing a number of simple residual diagnostic plots.

  18. Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems

    Directory of Open Access Journals (Sweden)

    Xinbo Zhang

    2014-01-01

    Full Text Available A polymorphic uncertain linear programming (PULP model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems.

  19. Generalized space and linear momentum operators in quantum mechanics

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Bruno G. da, E-mail: bruno.costa@ifsertao-pe.edu.br [Instituto Federal de Educação, Ciência e Tecnologia do Sertão Pernambucano, Campus Petrolina, BR 407, km 08, 56314-520 Petrolina, Pernambuco (Brazil); Instituto de Física, Universidade Federal da Bahia, R. Barão de Jeremoabo s/n, 40170-115 Salvador, Bahia (Brazil); Borges, Ernesto P., E-mail: ernesto@ufba.br [Instituto de Física, Universidade Federal da Bahia, R. Barão de Jeremoabo s/n, 40170-115 Salvador, Bahia (Brazil)

    2014-06-15

    We propose a modification of a recently introduced generalized translation operator, by including a q-exponential factor, which implies in the definition of a Hermitian deformed linear momentum operator p{sup ^}{sub q}, and its canonically conjugate deformed position operator x{sup ^}{sub q}. A canonical transformation leads the Hamiltonian of a position-dependent mass particle to another Hamiltonian of a particle with constant mass in a conservative force field of a deformed phase space. The equation of motion for the classical phase space may be expressed in terms of the generalized dual q-derivative. A position-dependent mass confined in an infinite square potential well is shown as an instance. Uncertainty and correspondence principles are analyzed.

  20. Generalized Ghost Dark Energy with Non-Linear Interaction

    CERN Document Server

    Ebrahimi, E; Mehrabi, A; Movahed, S M S

    2016-01-01

    In this paper we investigate ghost dark energy model in the presence of non-linear interaction between dark energy and dark matter. The functional form of dark energy density in the generalized ghost dark energy (GGDE) model is $\\rho_D\\equiv f(H, H^2)$ with coefficient of $H^2$ represented by $\\zeta$ and the model contains three free parameters as $\\Omega_D, \\zeta$ and $b^2$ (the coupling coefficient of interactions). We propose three kinds of non-linear interaction terms and discuss the behavior of equation of state, deceleration and dark energy density parameters of the model. We also find the squared sound speed and search for signs of stability of the model. To compare the interacting GGDE model with observational data sets, we use more recent observational outcomes, namely SNIa, gamma-ray bursts, baryonic acoustic oscillation and the most relevant CMB parameters including, the position of acoustic peaks, shift parameters and redshift to recombination. For GGDE with the first non-linear interaction, the j...

  1. Linear spin-2 fields in most general backgrounds

    CERN Document Server

    Bernard, Laura; Schmidt-May, Angnis; von Strauss, Mikael

    2015-01-01

    We derive the full perturbative equations of motion for the most general background solutions in ghost-free bimetric theory in its metric formulation. Clever field redefinitions at the level of fluctuations enable us to circumvent the problem of varying a square-root matrix appearing in the theory. This greatly simplifies the expressions for the linear variation of the bimetric interaction terms. We show that these field redefinitions exist and are uniquely invertible if and only if the variation of the square-root matrix itself has a unique solution, which is a requirement for the linearised theory to be well-defined. As an application of our results we examine the constraint structure of ghost-free bimetric theory at the level of linear equations of motion for the first time. We identify a scalar combination of equations which is responsible for the absence of the Boulware-Deser ghost mode in the theory. The bimetric scalar constraint is in general not manifestly covariant in its nature. However, in the mas...

  2. General quantum constraints on detector noise in continuous linear measurements

    Science.gov (United States)

    Miao, Haixing

    2017-01-01

    In quantum sensing and metrology, an important class of measurement is the continuous linear measurement, in which the detector is coupled to the system of interest linearly and continuously in time. One key aspect involved is the quantum noise of the detector, arising from quantum fluctuations in the detector input and output. It determines how fast we acquire information about the system and also influences the system evolution in terms of measurement backaction. We therefore often categorize it as the so-called imprecision noise and quantum backaction noise. There is a general Heisenberg-like uncertainty relation that constrains the magnitude of and the correlation between these two types of quantum noise. The main result of this paper is to show that, when the detector becomes ideal, i.e., at the quantum limit with minimum uncertainty, not only does the uncertainty relation takes the equal sign as expected, but also there are two new equalities. This general result is illustrated by using the typical cavity QED setup with the system being either a qubit or a mechanical oscillator. Particularly, the dispersive readout of a qubit state, and the measurement of mechanical motional sideband asymmetry are considered.

  3. How adaptation and mass transfer control the biodegradation of linear alkylbenzene sulfonate by activated sludge.

    Science.gov (United States)

    Rittmann, B E; Tularak, P; Lee, K C; Federle, T W; Itrich, N R; Kaiser, S K; Shi, J; McAvoy, D C

    2001-01-01

    We use a nonsteady-state model to evaluate the effects of community adaptation and sorption kinetics on the fate of linear alkylbenzene sulfonate (LAS) in batch experiments conducted with activated sludge that was continuously fed different concentrations of LAS. We observed a sharp decrease in the biodegradation rate between 30 and 60 minutes and the presence of an LAS residual at the end of the batch experiments. The modeling analysis indicates that these phenomena were caused by relatively slow inter-phase mass transport of LAS. The modeling analyses also showed that the amount of LAS-degrading biomass increased when the continuous activated sludge was fed a higher LAS concentration. Although community adaptation to LAS involved accumulation of more LAS degraders, the increase was not proportional to the feed concentration of LAS, which supports the concept that LAS degraders also utilized portions of the general biochemical oxygen demand (BOD) fed to the continuous activated sludge systems.

  4. Plasticity-rigidity cycles: A general adaptation mechanism

    OpenAIRE

    Csermely, Peter

    2015-01-01

    Successful adaptation helped the emergence of complexity. Alternating plastic- and rigid-like states were recurrently considered to play a role in adaptive processes. However, this extensive knowledge remained fragmented. In this paper I describe plasticity-rigidity cycles as a general adaptation mechanism operating in molecular assemblies, assisted protein folding, cellular differentiation, learning, memory formation, creative thinking, as well as the organization of social groups and ecosys...

  5. Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill present and future aircraft safety objectives though automated vehicle recovery while maintaining performance and...

  6. Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill aircraft safety objectives though automated vehicle recovery while maintaining performance and stability...

  7. General Intelligence as a Domain-Specific Adaptation

    Science.gov (United States)

    Kanazawa, Satoshi

    2004-01-01

    General intelligence (g) poses a problem for evolutionary psychology's modular view of the human brain. The author advances a new evolutionary psychological theory of the evolution of general intelligence and argues that general intelligence evolved as a domain-specific adaptation for the originally limited sphere of evolutionary novelty in the…

  8. Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models.

    Science.gov (United States)

    Xie, Minge; Simpson, Douglas G; Carroll, Raymond J

    2008-01-01

    This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric components of the models. Such models provide a means to adapt for heterogeneity in the data due to varying exposures, varying levels of aggregation, and so on. The class of models considered includes generalized partially linear models and nonparametrically scaled link function models as special cases. We present an algorithm to estimate the scale function nonparametrically, and obtain asymptotic distribution theory for regression parameter estimates. In particular, we establish that the asymptotic covariance of the semiparametric estimator for the parametric part of the model achieves the semiparametric lower bound. We also describe bootstrap-based goodness-of-scale test. We illustrate the methodology with simulations, published data, and data from collaborative research on ultrasound safety.

  9. Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    KAUST Repository

    Liang, Faming

    2013-06-01

    This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  10. A new family of gauges in linearized general relativity

    Science.gov (United States)

    Esposito, Giampiero; Stornaiolo, Cosimo

    2000-05-01

    For vacuum Maxwell theory in four dimensions, a supplementary condition exists (due to Eastwood and Singer) which is invariant under conformal rescalings of the metric, in agreement with the conformal symmetry of the Maxwell equations. Thus, starting from the de Donder gauge, which is not conformally invariant but is the gravitational counterpart of the Lorenz gauge, one can consider, led by formal analogy, a new family of gauges in general relativity, which involve fifth-order covariant derivatives of metric perturbations. The admissibility of such gauges in the classical theory is first proven in the cases of linearized theory about flat Euclidean space or flat Minkowski spacetime. In the former, the general solution of the equation for the fulfillment of the gauge condition after infinitesimal diffeomorphisms involves a 3-harmonic 1-form and an inverse Fourier transform. In the latter, one needs instead the kernel of powers of the wave operator, and a contour integral. The analysis is also used to put restrictions on the dimensionless parameter occurring in the DeWitt supermetric, while the proof of admissibility is generalized to a suitable class of curved Riemannian backgrounds. Eventually, a non-local construction of the tensor field is obtained which makes it possible to achieve conformal invariance of the above gauges.

  11. Explicit estimating equations for semiparametric generalized linear latent variable models

    KAUST Repository

    Ma, Yanyuan

    2010-07-05

    We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.

  12. Modeling local item dependence with the hierarchical generalized linear model.

    Science.gov (United States)

    Jiao, Hong; Wang, Shudong; Kamata, Akihito

    2005-01-01

    Local item dependence (LID) can emerge when the test items are nested within common stimuli or item groups. This study proposes a three-level hierarchical generalized linear model (HGLM) to model LID when LID is due to such contextual effects. The proposed three-level HGLM was examined by analyzing simulated data sets and was compared with the Rasch-equivalent two-level HGLM that ignores such a nested structure of test items. The results demonstrated that the proposed model could capture LID and estimate its magnitude. Also, the two-level HGLM resulted in larger mean absolute differences between the true and the estimated item difficulties than those from the proposed three-level HGLM. Furthermore, it was demonstrated that the proposed three-level HGLM estimated the ability distribution variance unaffected by the LID magnitude, while the two-level HGLM with no LID consideration increasingly underestimated the ability variance as the LID magnitude increased.

  13. dglars: An R Package to Estimate Sparse Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    Luigi Augugliaro

    2014-09-01

    Full Text Available dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013, developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013, and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012. The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.

  14. Analysis of Robust Quasi-deviances for Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    Eva Cantoni

    2004-04-01

    Full Text Available Generalized linear models (McCullagh and Nelder 1989 are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . . , n, come from a distribution belonging to the exponential family, such that E[Yi ] = ?i and V[Yi ] = V (?i , and that ?i = g(?i = xiT?, where ? ? IR p is the vector of parameters, xi ? IR p, and g(. is the link function. The non-robustness of the maximum likelihood and the maximum quasi-likelihood estimators has been studied extensively in the literature. For model selection, the classical analysis-of-deviance approach shares the same bad robustness properties. To cope with this, Cantoni and Ronchetti (2001 propose a robust approach based on robust quasi-deviance functions for estimation and variable selection. We refer to that paper for a deeper discussion and the review of the literature.

  15. Mixed Task and Data Parallel Executions in General Linear Methods

    Directory of Open Access Journals (Sweden)

    Thomas Rauber

    2007-01-01

    Full Text Available On many parallel target platforms it can be advantageous to implement parallel applications as a collection of multiprocessor tasks that are concurrently executed and are internally implemented with fine-grain SPMD parallelism. A class of applications which can benefit from this programming style are methods for solving systems of ordinary differential equations. Many recent solvers have been designed with an additional potential of method parallelism, but the actual effectiveness of mixed task and data parallelism depends on the specific communication and computation requirements imposed by the equation to be solved. In this paper we study mixed task and data parallel implementations for general linear methods realized using a library for multiprocessor task programming. Experiments on a number of different platforms show good efficiency results.

  16. Dual adaptation and adaptive generalization of the human vestibulo-ocular reflex

    Science.gov (United States)

    Welch, R. B.; Bridgeman, B.; Williams, J. A.; Semmler, R.

    1998-01-01

    In two experiments, we examined the possibility that the human vestibulo-ocular reflex (VOR) is subject to dual adaptation (the ability to adapt to a sensory rearrangement more rapidly and/or more completely after repeated experience with it) and adaptive generalization (the ability to adapt more readily to a novel sensory rearrangement as a result of prior dual adaptation training). In Experiment 1, the subjects actively turned the head during alternating exposure to a visual-vestibular rearrangement (target/head gain = 0.5) and the normal situation (target/head gain = 0.0). These conditions produced both adaptation and dual adaptation of the VOR but no evidence of adaptive generalization when tested with a target/head gain of 1.0. Experiment 2, in which exposure to the 0.5 gain entailed externally controlled (i.e., passive) whole body rotation, resulted in VOR adaptation but no dual adaptation. As in Experiment 1, no evidence of adaptive generalization was found.

  17. Adaptive Non-linear Control of Hydraulic Actuator Systems

    DEFF Research Database (Denmark)

    Hansen, Poul Erik; Conrad, Finn

    1998-01-01

    Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....

  18. Estimate of influenza cases using generalized linear, additive and mixed models.

    Science.gov (United States)

    Oviedo, Manuel; Domínguez, Ángela; Pilar Muñoz, M

    2015-01-01

    We investigated the relationship between reported cases of influenza in Catalonia (Spain). Covariates analyzed were: population, age, data of report of influenza, and health region during 2010-2014 using data obtained from the SISAP program (Institut Catala de la Salut - Generalitat of Catalonia). Reported cases were related with the study of covariates using a descriptive analysis. Generalized Linear Models, Generalized Additive Models and Generalized Additive Mixed Models were used to estimate the evolution of the transmission of influenza. Additive models can estimate non-linear effects of the covariates by smooth functions; and mixed models can estimate data dependence and variability in factor variables using correlations structures and random effects, respectively. The incidence rate of influenza was calculated as the incidence per 100 000 people. The mean rate was 13.75 (range 0-27.5) in the winter months (December, January, February) and 3.38 (range 0-12.57) in the remaining months. Statistical analysis showed that Generalized Additive Mixed Models were better adapted to the temporal evolution of influenza (serial correlation 0.59) than classical linear models.

  19. A New Family of Gauges in Linearized General Relativity

    CERN Document Server

    Esposito, G; Esposito, Giampiero; Stornaiolo, Cosimo

    2000-01-01

    For vacuum Maxwell theory in four dimensions, a supplementary condition exists (due to Eastwood and Singer) which is invariant under conformal rescalings of the metric, in agreement with the conformal symmetry of the Maxwell equations. Thus, starting from the de Donder gauge, which is not conformally invariant but is the gravitational counterpart of the Lorenz gauge, one can consider, led by formal analogy, a new family of gauges in general relativity, which involve fifth-order covariant derivatives of metric perturbations. The admissibility of such gauges in the classical theory is here proven in the cases of linearized theory about flat Euclidean space or flat Minkowski space-time. In the former, the general solution of the equation for the fulfillment of the gauge condition after infinitesimal diffeomorphisms involves a 3-harmonic function and an inverse Fourier transform. In the latter, one needs instead the kernel of powers of the wave operator, and a contour integral. The analysis is also used to put re...

  20. Generalized linear models with coarsened covariates: a practical Bayesian approach.

    Science.gov (United States)

    Johnson, Timothy R; Wiest, Michelle M

    2014-06-01

    Coarsened covariates are a common and sometimes unavoidable phenomenon encountered in statistical modeling. Covariates are coarsened when their values or categories have been grouped. This may be done to protect privacy or to simplify data collection or analysis when researchers are not aware of their drawbacks. Analyses with coarsened covariates based on ad hoc methods can compromise the validity of inferences. One valid method for accounting for a coarsened covariate is to use a marginal likelihood derived by summing or integrating over the unknown realizations of the covariate. However, algorithms for estimation based on this approach can be tedious to program and can be computationally expensive. These are significant obstacles to their use in practice. To overcome these limitations, we show that when expressed as a Bayesian probability model, a generalized linear model with a coarsened covariate can be posed as a tractable missing data problem where the missing data are due to censoring. We also show that this model is amenable to widely available general-purpose software for simulation-based inference for Bayesian probability models, providing researchers a very practical approach for dealing with coarsened covariates.

  1. L1-norm locally linear representation regularization multi-source adaptation learning.

    Science.gov (United States)

    Tao, Jianwen; Wen, Shiting; Hu, Wenjun

    2015-09-01

    In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object.

  2. Synchronization of generalized Henon map by using adaptive fuzzy controller

    CERN Document Server

    Xue Yue Ju

    2003-01-01

    In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization.

  3. Generalized linear longitudinal mixed models with linear covariance structure and multiplicative random effects

    DEFF Research Database (Denmark)

    Holst, René; Jørgensen, Bent

    2015-01-01

    The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....

  4. Nonlinear System Design: Adaptive Feedback Linearization with Unmodeled Dynamics

    Science.gov (United States)

    1991-09-30

    First, we address severe restrictions of the two currently available types of the regulation problem . In Section 11 we characterize the schemes: the...existence of such a Lyapunov II. THE CLASS OF NONLINEAR SYSTEMS function cannot be aserned a priori. fa . The adaptive regulation problem will first be

  5. Identification and adaptive control scheme using fuzzy parameterized linear filters

    NARCIS (Netherlands)

    Papp, Z.

    1998-01-01

    A nonlinear fuzzy control structure enhanced with supervised learning and/or adaption is presented. Availability of at least a partial process model is assumed. Nonlinear process identification procedure is used to complete the partial model. Based on the identification model the system sensitivity

  6. Linear Perturbation Adaptive Control of Hydraulically Driven Manipulators

    DEFF Research Database (Denmark)

    Andersen, T.O.; Hansen, M.R.; Conrad, Finn

    2004-01-01

    control.Using the Lyapunov approach, under slowly time-varying assumptions, it is shown that the tracking error and the parameter error remain bounded. This bound is a function of the ideal parameters and a bounded disturbance. The control algorithm decouples and linearizes the manipulator so that each...

  7. An Adaptive Linear Approximation Algorithm for Copositive Programs

    NARCIS (Netherlands)

    Bundfuss, Stefan; Dur, Mirjam

    2009-01-01

    We study linear optimization problems over the cone of copositive matrices. These problems appear in nonconvex quadratic and binary optimization; for instance, the maximum clique problem and other combinatorial problems can be reformulated as such problems. We present new polyhedral inner and outer

  8. Synchronization of general complex networks via adaptive control schemes

    Indian Academy of Sciences (India)

    Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik

    2014-03-01

    In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.

  9. The linearized inversion of the generalized interferometric multiple imaging

    KAUST Repository

    Aldawood, Ali

    2016-09-06

    The generalized interferometric multiple imaging (GIMI) procedure can be used to image duplex waves and other higher order internal multiples. Imaging duplex waves could help illuminate subsurface zones that are not easily illuminated by primaries such as vertical and nearly vertical fault planes, and salt flanks. To image first-order internal multiple, the GIMI framework consists of three datuming steps, followed by applying the zero-lag cross-correlation imaging condition. However, the standard GIMI procedure yields migrated images that suffer from low spatial resolution, migration artifacts, and cross-talk noise. To alleviate these problems, we propose a least-squares GIMI framework in which we formulate the first two steps as a linearized inversion problem when imaging first-order internal multiples. Tests on synthetic datasets demonstrate the ability to localize subsurface scatterers in their true positions, and delineate a vertical fault plane using the proposed method. We, also, demonstrate the robustness of the proposed framework when imaging the scatterers or the vertical fault plane with erroneous migration velocities.

  10. Bayesian inference for generalized linear models for spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2010-05-01

    Full Text Available Generalized Linear Models (GLMs are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate.

  11. Generalized Functional Linear Models With Semiparametric Single-Index Interactions

    KAUST Repository

    Li, Yehua

    2010-06-01

    We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.

  12. Multivariate statistical modelling based on generalized linear models

    CERN Document Server

    Fahrmeir, Ludwig

    1994-01-01

    This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

  13. Generalized linear model for estimation of missing daily rainfall data

    Science.gov (United States)

    Rahman, Nurul Aishah; Deni, Sayang Mohd; Ramli, Norazan Mohamed

    2017-04-01

    The analysis of rainfall data with no missingness is vital in various applications including climatological, hydrological and meteorological study. The issue of missing data is a serious concern since it could introduce bias and lead to misleading conclusions. In this study, five imputation methods including simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate were used to estimate the missing data. However, these imputation methods ignored the seasonality in rainfall dataset which could give more reliable estimation. Thus this study is aimed to estimate the missingness in daily rainfall data by using generalized linear model with gamma and Fourier series as the link function and smoothing technique, respectively. Forty years daily rainfall data for the period from 1975 until 2014 which consists of seven stations at Kelantan region were selected for the analysis. The findings indicated that the imputation methods could provide more accurate estimation values based on the least mean absolute error, root mean squared error and coefficient of variation root mean squared error when seasonality in the dataset are considered.

  14. An Adaptive Finite Element Method Based on Optimal Error Estimates for Linear Elliptic Problems

    Institute of Scientific and Technical Information of China (English)

    汤雁

    2004-01-01

    The subject of the work is to propose a series of papers about adaptive finite element methods based on optimal error control estimate. This paper is the third part in a series of papers on adaptive finite element methods based on optimal error estimates for linear elliptic problems on the concave corner domains. In the preceding two papers (part 1:Adaptive finite element method based on optimal error estimate for linear elliptic problems on concave corner domain; part 2:Adaptive finite element method based on optimal error estimate for linear elliptic problems on nonconvex polygonal domains), we presented adaptive finite element methods based on the energy norm and the maximum norm. In this paper, an important result is presented and analyzed. The algorithm for error control in the energy norm and maximum norm in part 1 and part 2 in this series of papers is based on this result.

  15. Adaptive Generation and Diagnostics of Linear Few-Cycle Light Bullets

    Directory of Open Access Journals (Sweden)

    Martin Bock

    2013-02-01

    Full Text Available Recently we introduced the class of highly localized wavepackets (HLWs as a generalization of optical Bessel-like needle beams. Here we report on the progress in this field. In contrast to pulsed Bessel beams and Airy beams, ultrashort-pulsed HLWs propagate with high stability in both spatial and temporal domain, are nearly paraxial (supercollimated, have fringe-less spatial profiles and thus represent the best possible approximation to linear “light bullets”. Like Bessel beams and Airy beams, HLWs show self-reconstructing behavior. Adaptive HLWs can be shaped by ultraflat three-dimensional phase profiles (generalized axicons which are programmed via calibrated grayscale maps of liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs. Light bullets of even higher complexity can either be freely formed from quasi-continuous phase maps or discretely composed from addressable arrays of identical nondiffracting beams. The characterization of few-cycle light bullets requires spatially resolved measuring techniques. In our experiments, wavefront, pulse and phase were detected with a Shack-Hartmann wavefront sensor, 2D-autocorrelation and spectral phase interferometry for direct electric-field reconstruction (SPIDER. The combination of the unique propagation properties of light bullets with the flexibility of adaptive optics opens new prospects for applications of structured light like optical tweezers, microscopy, data transfer and storage, laser fusion, plasmon control or nonlinear spectroscopy.

  16. Differential adaptation of the linear and nonlinear components of the horizontal vestibuloocular reflex in squirrel monkeys

    Science.gov (United States)

    Clendaniel, Richard A.; Lasker, David M.; Minor, Lloyd B.; Shelhamer, M. J. (Principal Investigator)

    2002-01-01

    Previous work in squirrel monkeys has demonstrated the presence of linear and nonlinear components to the horizontal vestibuloocular reflex (VOR) evoked by high-acceleration rotations. The nonlinear component is seen as a rise in gain with increasing velocity of rotation at frequencies more than 2 Hz (a velocity-dependent gain enhancement). We have shown that there are greater changes in the nonlinear than linear component of the response after spectacle-induced adaptation. The present study was conducted to determine if the two components of the response share a common adaptive process. The gain of the VOR, in the dark, to sinusoidal stimuli at 4 Hz (peak velocities: 20-150 degrees /s) and 10 Hz (peak velocities: 20 and 100 degrees /s) was measured pre- and postadaptation. Adaptation was induced over 4 h with x0.45 minimizing spectacles. Sum-of-sines stimuli were used to induce adaptation, and the parameters of the stimuli were adjusted to invoke only the linear or both linear and nonlinear components of the response. Preadaptation, there was a velocity-dependent gain enhancement at 4 and 10 Hz. In postadaptation with the paradigms that only recruited the linear component, there was a decrease in gain and a persistent velocity-dependent gain enhancement (indicating adaptation of only the linear component). After adaptation with the paradigm designed to recruit both the linear and nonlinear components, there was a decrease in gain and no velocity-dependent gain enhancement (indicating adaptation of both components). There were comparable changes in the response to steps of acceleration. We interpret these results to indicate that separate processes drive the adaptation of the linear and nonlinear components of the response.

  17. Massively parallel-in-space-time, adaptive finite element framework for non-linear parabolic equations

    CERN Document Server

    Dyja, Robert; van der Zee, Kristoffer G

    2016-01-01

    We present an adaptive methodology for the solution of (linear and) non-linear time dependent problems that is especially tailored for massively parallel computations. The basic concept is to solve for large blocks of space-time unknowns instead of marching sequentially in time. The methodology is a combination of a computationally efficient implementation of a parallel-in-space-time finite element solver coupled with a posteriori space-time error estimates and a parallel mesh generator. This methodology enables, in principle, simultaneous adaptivity in both space and time (within the block) domains. We explore this basic concept in the context of a variety of time-steppers including $\\Theta$-schemes and Backward Differentiate Formulas. We specifically illustrate this framework with applications involving time dependent linear, quasi-linear and semi-linear diffusion equations. We focus on investigating how the coupled space-time refinement indicators for this class of problems affect spatial adaptivity. Final...

  18. Non-linear and adaptive control of a refrigeration system

    DEFF Research Database (Denmark)

    Rasmussen, Henrik; Larsen, Lars F. S.

    2011-01-01

    In a refrigeration process heat is absorbed in an evaporator by evaporating a flow of liquid refrigerant at low pressure and temperature. Controlling the evaporator inlet valve and the compressor in such a way that a high degree of liquid filling in the evaporator is obtained at all compressor...... are capable of adapting to variety of systems. This paper proposes a novel method for superheat and capacity control of refrigeration systems; namely by controlling the superheat by the compressor speed and capacity by the refrigerant flow. A new low order nonlinear model of the evaporator is developed...

  19. NGPG-STABILITY OF LINEAR MULTISTEP METHODS FOR SYSTEMS OF GENERALIZED NEUTRAL DELAY DIFFERENTIAL EQUATIONS

    Institute of Scientific and Technical Information of China (English)

    丛玉豪

    2001-01-01

    The stability analysis of linear multistep methods for the numerical solutions of the systems of generalized neutral delay differential equations is discussed. The stability behaviour of linear multistep methods was analysed for the solution of the generalized system of linear neutral test equations. After the establishment of a sufficient condition for asymptotic stability of the solutions of the generalized system, it is shown that a linear multistep method is NGPG-stable if and only if it is A-stable.

  20. A covariance-adaptive approach for regularized inversion in linear models

    Science.gov (United States)

    Kotsakis, Christopher

    2007-11-01

    The optimal inversion of a linear model under the presence of additive random noise in the input data is a typical problem in many geodetic and geophysical applications. Various methods have been developed and applied for the solution of this problem, ranging from the classic principle of least-squares (LS) estimation to other more complex inversion techniques such as the Tikhonov-Philips regularization, truncated singular value decomposition, generalized ridge regression, numerical iterative methods (Landweber, conjugate gradient) and others. In this paper, a new type of optimal parameter estimator for the inversion of a linear model is presented. The proposed methodology is based on a linear transformation of the classic LS estimator and it satisfies two basic criteria. First, it provides a solution for the model parameters that is optimally fitted (in an average quadratic sense) to the classic LS parameter solution. Second, it complies with an external user-dependent constraint that specifies a priori the error covariance (CV) matrix of the estimated model parameters. The formulation of this constrained estimator offers a unified framework for the description of many regularization techniques that are systematically used in geodetic inverse problems, particularly for those methods that correspond to an eigenvalue filtering of the ill-conditioned normal matrix in the underlying linear model. Our study lies on the fact that it adds an alternative perspective on the statistical properties and the regularization mechanism of many inversion techniques commonly used in geodesy and geophysics, by interpreting them as a family of `CV-adaptive' parameter estimators that obey a common optimal criterion and differ only on the pre-selected form of their error CV matrix under a fixed model design.

  1. Adaptive Input-Output Linearization Technique for Robust Speed Control of Brush less DC Motor

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kyeong Hwa; Baik, In Cheol; Kim, Hyun Soo; Youn, Myung Joong [Korea Advance Institute of Science and Technology, Taejon (Korea, Republic of)

    1997-06-01

    An adaptive input-output linearization technique for a robust speed control of a brush less DC (BLDC) motor is presented. By using this technique, the nonlinear motor model can be effectively linearized in Brunovski canonical form, and the desired speed dynamics can be obtained based on the linearized model. This control technique, however, gives an undesirable output performance under the mismatch of the system parameters and load conditions caused by the incomplete linearization. For the robust output response, the controller parameters will be estimated by a model reference adaptive technique where the disturbance torque and flux linkage are estimated. The adaptation laws are derived by the Popov`s hyper stability theory and positivity concept. The proposed control scheme is implemented on a BLDC motor using the software of DSP TMS320C30 and the effectiveness is verified through the comparative simulations and experiments. (author). 14 refs., 12 figs., 1 tab.

  2. On Self-Adaptive Method for General Mixed Variational Inequalities

    Directory of Open Access Journals (Sweden)

    Abdellah Bnouhachem

    2008-01-01

    Full Text Available We suggest and analyze a new self-adaptive method for solving general mixed variational inequalities, which can be viewed as an improvement of the method of (Noor 2003. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given to illustrate the efficiency of the proposed method. Since the general mixed variational inequalities include general variational inequalities, quasivariational inequalities, and nonlinear (implicit complementarity problems as special cases, results proved in this paper continue to hold for these problems.

  3. Adaptive generalized functional synchronization of Chaotic systems with unknown parameters

    Institute of Scientific and Technical Information of China (English)

    Wang Dong-Feng; Han Pu

    2008-01-01

    A universal adaptive generalized functional synchronization approach to any two different or identical chaotic systems with unknown parameters is proposed,based on a unified mathematical expression of a large class of chaotic system.Self-adaptive parameter law and control law are given in the form of a theorem.The synchronization between the three-dimensional R(o)ssler chaotic system and the four-dimensional Chen's hyper-chaotic system is studied as an example for illustration.The computer simulation results demonstrate the feasibility of the method proposed.

  4. Adaptive fault-tolerant control of linear systems with actuator saturation and L2-disturbances

    Institute of Scientific and Technical Information of China (English)

    Wei GUAN; Guanghong YANG

    2009-01-01

    This paper studies the problem of designing adaptive fault-tolerant H-infinity controllers for linear timeinvariant systems with actuator saturation. The disturbance tolerance ability of the closed-loop system is measured by an optimal index. The notion of an adaptive H-infinity performance index is proposed to describe the disturbance attenuation performances of closed-loop systems. New methods for designing indirect adaptive fault-tolerant controllers via state feedback are presented for actuator fault compensations. Based on the on-line estimation of eventual faults, the adaptive fault-tolerant controller parameters are updated automatically to compensate for the fault effects on systems. The designs are developed in the framework of the linear matrix inequality (LMI) approach, which can guarantee the disturbance tolerance ability and adaptive H-infinity performances of closed-loop systems in the cases of actuator saturation and actuator failures. An example is given to illustrate the efficiency of the design method.

  5. GENERALIZED RICCATI TRANSFORMATION AND OSCILLATION FOR LINEAR DIFFERENTIAL EQUATIONS WITH DAMPING

    Institute of Scientific and Technical Information of China (English)

    ZhengZhaowen; LiuJingzhao

    2005-01-01

    Using generalized Riccati transformation, some new oscillation criteria for damped linear differential equations are established. These results improve and generalize some known oscillation criteria due to A.Wintner [8], I.V.Kamenev [10] for the undamped linear differential equations, and Sobol [3], J.S.W.Wong [1] for the damped linear differential equations.

  6. Admissible Estimators in the General Multivariate Linear Model with Respect to Inequality Restricted Parameter Set

    Directory of Open Access Journals (Sweden)

    Liu Gang

    2009-01-01

    Full Text Available By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are established.

  7. Generalized (,,-Pairs for Uncertain Linear Infinite-Dimensional Systems

    Directory of Open Access Journals (Sweden)

    Naohisa Otsuka

    2009-01-01

    Full Text Available We introduce the concept of generalized (,,-pairs which is related to generalized (,-invariant subspaces and generalized (,-invariant subspaces for infinite-dimensional systems. As an application the parameter-insensitive disturbance-rejection problem with dynamic compensator is formulated and its solvability conditions are presented. Further, an illustrative example is also examined.

  8. Connections between Generalizing and Justifying: Students' Reasoning with Linear Relationships

    Science.gov (United States)

    Ellis, Amy B.

    2007-01-01

    Research investigating algebra students' abilities to generalize and justify suggests that they experience difficulty in creating and using appropriate generalizations and proofs. Although the field has documented students' errors, less is known about what students do understand to be general and convincing. This study examines the ways in which…

  9. Generalization in adaptation to stable and unstable dynamics.

    Directory of Open Access Journals (Sweden)

    Abdelhamid Kadiallah

    Full Text Available Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.

  10. Extended generalized Lagrangian multipliers for magnetohydrodynamics using adaptive multiresolution methods

    Directory of Open Access Journals (Sweden)

    Domingues M. O.

    2013-12-01

    Full Text Available We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution.

  11. A General Linear Method for Equating with Small Samples

    Science.gov (United States)

    Albano, Anthony D.

    2015-01-01

    Research on equating with small samples has shown that methods with stronger assumptions and fewer statistical estimates can lead to decreased error in the estimated equating function. This article introduces a new approach to linear observed-score equating, one which provides flexible control over how form difficulty is assumed versus estimated…

  12. PYESSENCE: Generalized Coupled Quintessence Linear Perturbation Python Code

    Science.gov (United States)

    Leithes, Alexander

    2016-09-01

    PYESSENCE evolves linearly perturbed coupled quintessence models with multiple (cold dark matter) CDM fluid species and multiple DE (dark energy) scalar fields, and can be used to generate quantities such as the growth factor of large scale structure for any coupled quintessence model with an arbitrary number of fields and fluids and arbitrary couplings.

  13. Linearizability of Nonlinear Third-Order Ordinary Differential Equations by Using a Generalized Linearizing Transformation

    OpenAIRE

    Thailert, E.; Suksern, S.

    2014-01-01

    We discuss the linearization problem of third-order ordinary differential equation under the generalized linearizing transformation. We identify the form of the linearizable equations and the conditions which allow the third-order ordinary differential equation to be transformed into the simplest linear equation. We also illustrate how to construct the generalized linearizing transformation. Some examples of linearizable equation are provided to demonstrate our procedure.

  14. Maximum Likelihood in a Generalized Linear Finite Mixture Model by Using the EM Algorithm

    NARCIS (Netherlands)

    Jansen, R.C.

    A generalized linear finite mixture model and an EM algorithm to fit the model to data are described. By this approach the finite mixture model is embedded within the general framework of generalized linear models (GLMs). Implementation of the proposed EM algorithm can be readily done in statistical

  15. Generalized linear IgA dermatosis with palmar involvement

    OpenAIRE

    Norris, Ivy N; Haeberle, M Tye; Callen, Jeffrey P.; Malone, Janine C

    2015-01-01

    Linear IgA bullous dermatosis (LABD) is a sub-epidermal blistering disorder characterized by deposition of IgA along the basement membrane zone (BMZ) as detected by immunofluorescence microscopy. The diagnosis is made by clinicopathologic correlation with immunofluorescence confirmation. Differentiation from other bullous dermatoses is important because therapeutic measures differ. Prompt initiation of the appropriate therapies can have a major impact on outcomes. We present three cases with ...

  16. Generalized in situ adaptive tabulation for constitutive model evaluation in plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Arsenlis, A; Barton, N; Becker, R; Rudd, R

    2005-04-28

    A database storage, search and retrieval method of constitutive model responses for use in plasticity simulations is developed to increase the computational efficiency of finite element simulations employing complex non-linear material models. The method is based in the in situ adaptive tabulation method that has been successfully applied in the field of combustion chemistry, but is significantly modified to better handle the system of equations in plasticity. When using the database, the material response is estimated by a linear extrapolation from an appropriate database entry. This is shown to provide a response with an acceptable error tolerance. Two different example problems are chosen to demonstrate the behavior of the constitutive model estimation technique: a dynamic shock simulation, and a quasi-static inhomogeneous deformation simulation. This generalized in situ adaptive tabulation method shows promise for enabling simulations with complex multi-physics and multi-length scale constitutive descriptions.

  17. Adaptive stabilization of discrete-time systems using linear periodically time varying controllers

    Science.gov (United States)

    Ortega, Romeo; Albertos, Pedro; Lozano, Rogelio

    1988-01-01

    A direct adaptive scheme based on the use of linear time-varying periodic controllers is proposed which estimates online the periodic coefficients of the controller. It is shown that adaptive stabilization is attained for all possibly nonstably invertible plants of known order but unknown delay. Although no appeal is made to persistency of excitation arguments, a provision is needed to avoid the singularity of an estimated matrix, this property being required only for the analysis and not the control calculations.

  18. Performance study of Active Queue Management methods: Adaptive GRED, REDD, and GRED-Linear analytical model

    Directory of Open Access Journals (Sweden)

    Hussein Abdel-jaber

    2015-10-01

    Full Text Available Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED, Random Early Dynamic Detection (REDD, and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping.

  19. Convergence of an adaptive Ka\\v{c}anov FEM for quasi-linear problems

    OpenAIRE

    Garau, Eduardo M.; Morin, Pedro; Zuppa, Carlos

    2010-01-01

    We design an adaptive finite element method to approximate the solutions of quasi-linear elliptic problems. The algorithm is based on a Ka\\v{c}anov iteration and a mesh adaptation step is performed after each linear solve. The method is thus \\emph{inexact} because we do not solve the discrete nonlinear problems exactly, but rather perform one iteration of a fixed point method (Ka\\v{c}anov), using the approximation of the previous mesh as an initial guess. The convergence of the method is prov...

  20. ADAPTIVE BLOCK QMRIOM(q)METHOD FOR SOLVING UNSYMMETRIC LINEAR SYSTEMS WITH MULTI

    Institute of Scientific and Technical Information of China (English)

    WangZhengsheng

    2002-01-01

    Many applications require the solution of large un-symmetric linear systems with multiple right-hand sides.Instead of applying an iterative method to each of these systems individually,it is often more efficient to use a block version of the method that generates iterates for all the systems simultaneously.This paper proposes a new adaptive block QMR version based on the incomplete or-thogomalization method(IOM(q))for solving large multi-ple nusymmetric linear systems.How to incorporate de-flation to drop comverged linear systems,and how to delete linearly and almost liearly dependent vectors in the underlying block Krylov sequences are discussed.Nu-merical experiments show that the new adaptive block method has better practical performance and less compu-tational cost and CPU time than block GMRES and other proposed methods for the solution of systems with multi- ple right-hand sides.

  1. PLC Based Adaptive PID Control of Non Linear Liquid Tank System using Online Estimation of Linear Parameters by Difference Equations

    Directory of Open Access Journals (Sweden)

    Kesavan.E

    2013-04-01

    Full Text Available This paper suggests an idea to design an adaptive PID controller for Non-linear liquid tank System and is implemented in PLC. Online estimation of linear parameters (Time constant and Gain brings an exact model of the process to take perfect control action. Based on these estimated values, the controller parameters will be well tuned by internal model control. Internal model control is an unremarkably used technique and provides well tuned controller in order to have a good controlling process. PLC with its ability to have both continues control for PID Control and digital control for fault diagnosis which ascertains faults in the system and provides alerts about the status of the entire process.

  2. Generalized linear IgA dermatosis with palmar involvement.

    Science.gov (United States)

    Norris, Ivy N; Haeberle, M Tye; Callen, Jeffrey P; Malone, Janine C

    2015-09-17

    Linear IgA bullous dermatosis (LABD) is a sub-epidermal blistering disorder characterized by deposition of IgA along the basement membrane zone (BMZ) as detected by immunofluorescence microscopy. The diagnosis is made by clinicopathologic correlation with immunofluorescence confirmation. Differentiation from other bullous dermatoses is important because therapeutic measures differ. Prompt initiation of the appropriate therapies can have a major impact on outcomes. We present three cases with prominent palmar involvement to alert the clinician of this potential physical exam finding and to consider LABD in the right context.

  3. Generalized linear mixed models modern concepts, methods and applications

    CERN Document Server

    Stroup, Walter W

    2012-01-01

    PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data

  4. Adaptive Control for Linear Uncertain Systems with Unmodeled Dynamics Revisited via Optimal Control Modification

    Science.gov (United States)

    Nguyen, Nhan

    2013-01-01

    This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.

  5. An adaptive linear combiner for on-line tracking of power system harmonics

    Energy Technology Data Exchange (ETDEWEB)

    Dash, P.K.; Swain, D.P. [Regional Engineering Coll., Rourkela (India). Dept. of Electrical Engineering; Liew, A.C. [National Univ. of Singapore (Singapore). Dept. of Electrical Engineering; Rahman, S. [Virginia Polytechnic Inst. and State Univ., VA (United States). Dept. of Electrical Engineering

    1996-11-01

    The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying dc components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying dc components.

  6. A general algorithm for computing distance transforms in linear time

    NARCIS (Netherlands)

    Meijster, A.; Roerdink, J.B.T.M.; Hesselink, W.H.; Goutsias, J; Vincent, L; Bloomberg, DS

    2000-01-01

    A new general algorithm fur computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the com

  7. A general algorithm for computing distance transforms in linear time

    NARCIS (Netherlands)

    Meijster, A.; Roerdink, J.B.T.M.; Hesselink, W.H.; Goutsias, J; Vincent, L; Bloomberg, DS

    2000-01-01

    A new general algorithm fur computing distance transforms of digital images is presented. The algorithm consists of two phases. Both phases consist of two scans, a forward and a backward scan. The first phase scans the image column-wise, while the second phase scans the image row-wise. Since the

  8. The general RF tuning for IH-DTL linear accelerators

    Science.gov (United States)

    Lu, Y. R.; Ratzinger, U.; Schlitt, B.; Tiede, R.

    2007-11-01

    The RF tuning is the most important research for achieving the resonant frequency and the flatness of electric field distributions along the axis of RF accelerating structures. The six different tuning concepts and that impacts on the longitudinal field distributions have been discussed in detail combining the RF tuning process of a 1:2 modeled 20.85 MV compact IH-DTL cavity, which was designed to accelerate proton, helium, oxygen or C 4+ from 400 keV/ u to 7 MeV/u and used as the linear injector of 430 MeV/ u synchrotron [Y.R. Lu, S. Minaev, U. Ratzinger, B. Schlitt, R.Tiede, The Compact 20MV IH-DTL for the Heidelberg Therapy Facility, in: Proceedings of the LINAC Conference, Luebeck, Germany, 2004 [1]; Y.R. Lu, Frankfurt University Dissertation, 2005. [2

  9. Adaptive Wavelet Methods for Linear and Nonlinear Least-Squares Problems

    NARCIS (Netherlands)

    Stevenson, R.

    2014-01-01

    The adaptive wavelet Galerkin method for solving linear, elliptic operator equations introduced by Cohen et al. (Math Comp 70:27-75, 2001) is extended to nonlinear equations and is shown to converge with optimal rates without coarsening. Moreover, when an appropriate scheme is available for the appr

  10. The general RF tuning for IH-DTL linear accelerators

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Y.R. [Key State Laboratory of Nuclear Physics and Technology, Peking University (China)], E-mail: yrlu@pku.edu.cn; Ratzinger, U. [Institute of Applied Physics, Frankfurt University (Germany); Schlitt, B. [Gesellschaft fuer Schwerionenforschung, mbH, Darmstadt (Germany); Tiede, R. [Institute of Applied Physics, Frankfurt University (Germany)

    2007-11-21

    The RF tuning is the most important research for achieving the resonant frequency and the flatness of electric field distributions along the axis of RF accelerating structures. The six different tuning concepts and that impacts on the longitudinal field distributions have been discussed in detail combining the RF tuning process of a 1:2 modeled 20.85 MV compact IH-DTL cavity, which was designed to accelerate proton, helium, oxygen or C{sup 4+} from 400 keV/u to 7 MeV/u and used as the linear injector of 430 MeV/u synchrotron [Y.R. Lu, S. Minaev, U. Ratzinger, B. Schlitt, R.Tiede, The Compact 20MV IH-DTL for the Heidelberg Therapy Facility, in: Proceedings of the LINAC Conference, Luebeck, Germany, 2004 ; Y.R. Lu, Frankfurt University Dissertation, 2005. ] in Heidelberg Heavy Ion Cancer Therapy (HICAT). Some of tuning concepts are also suitable and effective for the tuning of RFQ and/or other RF accelerating structures. Finally good field flatness in IH-DTL cavity has been realized successfully. The experience got from the model cavity tuning benefits real power cavity tuning, which is only needed to be tuned by the plungers. The cavity had a beam commissioning successfully for the initial beam acceleration at the end of 2006.

  11. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models

    Science.gov (United States)

    Rudy, Ashley C. A.; Lamoureux, Scott F.; Treitz, Paul; van Ewijk, Karin Y.

    2016-07-01

    To effectively assess and mitigate risk of permafrost disturbance, disturbance-prone areas can be predicted through the application of susceptibility models. In this study we developed regional susceptibility models for permafrost disturbances using a field disturbance inventory to test the transferability of the model to a broader region in the Canadian High Arctic. Resulting maps of susceptibility were then used to explore the effect of terrain variables on the occurrence of disturbances within this region. To account for a large range of landscape characteristics, the model was calibrated using two locations: Sabine Peninsula, Melville Island, NU, and Fosheim Peninsula, Ellesmere Island, NU. Spatial patterns of disturbance were predicted with a generalized linear model (GLM) and generalized additive model (GAM), each calibrated using disturbed and randomized undisturbed locations from both locations and GIS-derived terrain predictor variables including slope, potential incoming solar radiation, wetness index, topographic position index, elevation, and distance to water. Each model was validated for the Sabine and Fosheim Peninsulas using independent data sets while the transferability of the model to an independent site was assessed at Cape Bounty, Melville Island, NU. The regional GLM and GAM validated well for both calibration sites (Sabine and Fosheim) with the area under the receiver operating curves (AUROC) > 0.79. Both models were applied directly to Cape Bounty without calibration and validated equally with AUROC's of 0.76; however, each model predicted disturbed and undisturbed samples differently. Additionally, the sensitivity of the transferred model was assessed using data sets with different sample sizes. Results indicated that models based on larger sample sizes transferred more consistently and captured the variability within the terrain attributes in the respective study areas. Terrain attributes associated with the initiation of disturbances were

  12. Solution and applications of a class of general linear variational inequalities

    Institute of Scientific and Technical Information of China (English)

    何炳生

    1996-01-01

    Many problems in mathematical programming can be described as a general linear variational inequality of the following form: find a vector u*, such thatSome iterative methods for solving a class of general linear variational inequalities have been presented. It is pointed out that the methods can be used to solve some practical extended programming problems.

  13. The generalization of some trellis properties of linear codes to group codes

    Institute of Scientific and Technical Information of China (English)

    KAN HaiBin; LI XueFei; SHEN Hong

    2009-01-01

    In this paper, we discuss some trellis properties for codes over a finite Abelian group, which are the generalization of the corresponding trellis properties for linear codes over a field. Also, we also inves-tigate difficulties when we try to generalize a property of a tail-biting trellis for a linear code over a field to a group code.

  14. Rayleigh-type Surface Quasimodes in General Linear Elasticity

    CERN Document Server

    Hansen, Sönke

    2010-01-01

    Rayleigh-type surface waves correspond to the characteristic variety, in the elliptic boundary region, of the displacement-to-traction map. In this paper, surface quasimodes are constructed for the reduced elastic wave equation, anisotropic in general, with traction-free boundary. Assuming a global variant of a condition of Barnett and Lothe, the construction is reduced to an eigenvalue problem for a selfadjoint scalar first order pseudo-differential operator on the boundary. The principal and the subprincipal symbol of this operator are computed. The formula for the subprincipal symbol seems to be new even in the isotropic case.

  15. Decentralized adaptive generalized predictive control for structural vibration

    Institute of Scientific and Technical Information of China (English)

    LU Minyue; GU Zhongquan

    2005-01-01

    A decentralized generalized predictive control (GPC) algorithm is developed for strongly coupled multi-input multi-output systems with parallel computation. The algorithm is applied to adaptive control of structural vibration. The key steps in this algorithm are to group the actuators and the sensors and then to pair these groups into subsystems. It is important that the on-line identification and the control law design can be a parallel process for all these subsystems. It avoids the high computation cost in ordinary predictive control,and is of great advantage especially for large-scale systems.

  16. Blind adaptive MMSE equalization of underwater acoustic channels based on the linear prediction method

    Science.gov (United States)

    Zhang, Yinbing; Zhao, Junwei; Guo, Yecai; Li, Jinming

    2011-03-01

    The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method. Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification. Two methods for calculating linear MMSE equalizers were proposed. One was based on full channel identification and realized using RLS adaptive algorithms, and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms, respectively. Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels. The results show that the proposed algorithms are robust enough to channel order mismatch. They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

  17. Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

    Science.gov (United States)

    Ojo, Oluwatobi Blessing; Lougue, Siaka; Woldegerima, Woldegebriel Assefa

    2017-01-01

    TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.

  18. The left invariant metric in the general linear group

    CERN Document Server

    Andruchow, Esteban; Recht, Lazaro; Varela, Alejandro

    2011-01-01

    Left invariant metrics induced by the p-norms of the trace in the matrix algebra are studied on the general lineal group. By means of the Euler-Lagrange equations, existence and uniqueness of extremal paths for the length functional are established, and regularity properties of these extremal paths are obtained. Minimizing paths in the group are shown to have a velocity with constant singular values and multiplicity. In several special cases, these geodesic paths are computed explicitly. In particular the Riemannian geodesics, corresponding to the case p=2, are characterized as the product of two one-parameter groups. It is also shown that geodesics are one-parameter groups if and only if the initial velocity is a normal matrix. These results are further extended to the context of compact operators with p-summable spectrum, where a differential equation for the spectral projections of the velocity vector of an extremal path is obtained.

  19. Item Response Theory Using Hierarchical Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    Hamdollah Ravand

    2015-03-01

    Full Text Available Multilevel models (MLMs are flexible in that they can be employed to obtain item and person parameters, test for differential item functioning (DIF and capture both local item and person dependence. Papers on the MLM analysis of item response data have focused mostly on theoretical issues where applications have been add-ons to simulation studies with a methodological focus. Although the methodological direction was necessary as a first step to show how MLMs can be utilized and extended to model item response data, the emphasis needs to be shifted towards providing evidence on how applications of MLMs in educational testing can provide the benefits that have been promised. The present study uses foreign language reading comprehension data to illustrate application of hierarchical generalized models to estimate person and item parameters, differential item functioning (DIF, and local person dependence in a three-level model.

  20. Adaptive broadband beamformer for nonuniform linear array based on second order cone programming

    Institute of Scientific and Technical Information of China (English)

    Chen Peng; Hou Chaohuan; Ma Xiaochuan; Cao Zhiqian; Liang Yicong; Yan Sheng

    2009-01-01

    Adaptive broadband beamforming is a key issue in array applications. The adaptive broadband beamformer with tapped delay line (TDL) structure for nonuniform linear array (NLA) is designed according to the rule of minimizing the beamformer's output power while keeping the distortionless response (DR) in the direction of desired signal and keeping the constant beamwidth (CB) with the prescribed sidelobe level over the whole operating band. This kind of beamforming problem can be solved with the interior-point method after being converted to the form of standard second order cone programming (SOCP). The computer simulations are presented which illustrate the effectiveness of our bearaformer.

  1. On the General Taylor Theorem and its Applications in Solving Non—linear Problems

    Institute of Scientific and Technical Information of China (English)

    ShiJunLIAO

    1997-01-01

    In this paper,we propose a general Taylor series and prove a general Taylor theorem and then simply give some applications of it in solving non-linear differential equations.The general Taylor series is a family of power series which contains the classical Taylor series in logic.Moreover,it can be valid in much larger regions.

  2. GA and Lyapunov theory-based hybrid adaptive fuzzy controller for non-linear systems

    Science.gov (United States)

    Roy, Ananya; Das Sharma, Kaushik

    2015-02-01

    In this present article, a new hybrid methodology for designing stable adaptive fuzzy logic controllers (AFLCs) for a class of non-linear system is proposed. The proposed design strategy exploits the features of genetic algorithm (GA)-based stochastic evolutionary global search technique and Lyapunov theory-based local adaptation scheme. The objective is to develop a methodology for designing AFLCs with optimised free parameters and guaranteed closed-loop stability. Simultaneously, the proposed method introduces automation in the design process. The stand-alone Lyapunov theory-based design, GA-based design and proposed hybrid GA-Lyapunov design methodologies are implemented for two benchmark non-linear plants in simulation case studies with different reference signals and one experimental case study. The results demonstrate that the hybrid design methodology outperforms the other control strategies on the whole.

  3. An efficient method for generalized linear multiplicative programming problem with multiplicative constraints

    OpenAIRE

    Zhao, Yingfeng; Liu, Sanyang

    2016-01-01

    We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of so...

  4. An adaptive locally linear embedding manifold learning approach for hyperspectral target detection

    Science.gov (United States)

    Ziemann, Amanda K.; Messinger, David W.

    2015-05-01

    Algorithms for spectral analysis commonly use parametric or linear models of the data. Research has shown, however, that hyperspectral data -- particularly in materially cluttered scenes -- are not always well-modeled by statistical or linear methods. Here, we propose an approach to hyperspectral target detection that is based on a graph theory model of the data and a manifold learning transformation. An adaptive nearest neighbor (ANN) graph is built on the data, and then used to implement an adaptive version of locally linear embedding (LLE). We artificially induce a target manifold and incorporate it into the adaptive LLE transformation. The artificial target manifold helps to guide the separation of the target data from the background data in the new, transformed manifold coordinates. Then, target detection is performed in the manifold space using Spectral Angle Mapper. This methodology is an improvement over previous iterations of this approach due to the incorporation of ANN, the artificial target manifold, and the choice of detector in the transformed space. We implement our approach in a spatially local way: the image is delineated into square tiles, and the detection maps are normalized across the entire image. Target detection results will be shown using laboratory-measured and scene-derived target data from the SHARE 2012 collect.

  5. Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jian; ZHUANG Yue-ting

    2007-01-01

    In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.

  6. Markov Chain Analysis of Cumulative Step-Size Adaptation on a Linear Constrained Problem.

    Science.gov (United States)

    Chotard, Alexandre; Auger, Anne; Hansen, Nikolaus

    2015-01-01

    This paper analyzes a (1, λ)-Evolution Strategy, a randomized comparison-based adaptive search algorithm optimizing a linear function with a linear constraint. The algorithm uses resampling to handle the constraint. Two cases are investigated: first, the case where the step-size is constant, and second, the case where the step-size is adapted using cumulative step-size adaptation. We exhibit for each case a Markov chain describing the behavior of the algorithm. Stability of the chain implies, by applying a law of large numbers, either convergence or divergence of the algorithm. Divergence is the desired behavior. In the constant step-size case, we show stability of the Markov chain and prove the divergence of the algorithm. In the cumulative step-size adaptation case, we prove stability of the Markov chain in the simplified case where the cumulation parameter equals 1, and discuss steps to obtain similar results for the full (default) algorithm where the cumulation parameter is smaller than 1. The stability of the Markov chain allows us to deduce geometric divergence or convergence, depending on the dimension, constraint angle, population size, and damping parameter, at a rate that we estimate. Our results complement previous studies where stability was assumed.

  7. ORDER RESULTS OF GENERAL LINEAR METHODS FOR MULTIPLY STIFF SINGULAR PERTURBATION PROBLEMS

    Institute of Scientific and Technical Information of China (English)

    Si-qing Gan; Geng Sun

    2002-01-01

    In this paper we analyze the error behavior of general linear methods applied to some classes of one-parameter multiply stiff singularly perturbed problems. We obtain the global error estimate of algebraically and diagonally stable general linear methods. The main result of this paper can be viewed as an extension of that obtained by Xiao [13] for the case of Runge-Kutta methods.

  8. New adaptive method to optimize the secondary reflector of linear Fresnel collectors

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Guangdong

    2017-03-01

    Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form, but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. The proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.

  9. Adaptive matching of the iota ring linear optics for space charge compensation

    Energy Technology Data Exchange (ETDEWEB)

    Romanov, A. [Fermilab; Bruhwiler, D. L. [RadiaSoft, Boulder; Cook, N. [RadiaSoft, Boulder; Hall, C. [RadiaSoft, Boulder

    2016-10-09

    Many present and future accelerators must operate with high intensity beams when distortions induced by space charge forces are among major limiting factors. Betatron tune depression of above approximately 0.1 per cell leads to significant distortions of linear optics. Many aspects of machine operation depend on proper relations between lattice functions and phase advances, and can be i proved with proper treatment of space charge effects. We implement an adaptive algorithm for linear lattice re matching with full account of space charge in the linear approximation for the case of Fermilab’s IOTA ring. The method is based on a search for initial second moments that give closed solution and, at the same predefined set of goals for emittances, beta functions, dispersions and phase advances at and between points of interest. Iterative singular value decomposition based technique is used to search for optimum by varying wide array of model parameters

  10. Code Development of Three-Dimensional General Relativistic Hydrodynamics with AMR(Adaptive-Mesh Refinement) and Results From Special and General Relativistic Hydrodynamic

    CERN Document Server

    Donmez, O

    2004-01-01

    In this paper, the general procedure to solve the General Relativistic Hydrodynamical(GRH) equations with Adaptive-Mesh Refinement (AMR) is presented. In order to achieve, the GRH equations are written in the conservation form to exploit their hyperbolic character. The numerical solutions of general relativistic hydrodynamic equations are done by High Resolution Shock Capturing schemes (HRSC), specifically designed to solve non-linear hyperbolic systems of conservation laws. These schemes depend on the characteristic information of the system. The Marquina fluxes with MUSCL left and right states are used to solve GRH equations. First, different test problems with uniform and AMR grids on the special relativistic hydrodynamics equations are carried out to verify the second order convergence of the code in 1D, 2D and 3D. Results from uniform and AMR grid are compared. It is found that adaptive grid does a better job when the number of resolution is increased. Second, the general relativistic hydrodynamical equa...

  11. Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

    Directory of Open Access Journals (Sweden)

    Wenya Zhou

    2014-01-01

    Full Text Available Attitude dynamic model of unmanned aerial vehicles (UAVs is multi-input multioutput (MIMO, strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range, and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.

  12. Finite element model for linear-elastic mixed mode loading using adaptive mesh strategy

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    An adaptive mesh finite element model has been developed to predict the crack propagation direction as well as to calculate the stress intensity factors (SIFs), under linear-elastic assumption for mixed mode loading application. The finite element mesh is generated using the advancing front method. In order to suit the requirements of the fracture analysis, the generation of the background mesh and the construction of singular elements have been added to the developed program. The adaptive remeshing process is carried out based on the posteriori stress error norm scheme to obtain an optimal mesh. Previous works of the authors have proposed techniques for adaptive mesh generation of 2D cracked models. Facilitated by the singular elements, the displacement extrapolation technique is employed to calculate the SIF. The fracture is modeled by the splitting node approach and the trajectory follows the successive linear extensions of each crack increment. The SIFs values for two different case studies were estimated and validated by direct comparisons with other researchers work.

  13. Univariate and multivariate general linear models theory and applications with SAS

    CERN Document Server

    Kim, Kevin

    2006-01-01

    Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral sciences.With revised examples that include options available using SAS 9.0, this expanded edition divides theory from applications within each chapter. Following an overview of the GLM, the book introduces unrestricted GLMs to analyze multiple regr

  14. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    Science.gov (United States)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  15. Adaptive Elastic Net for Generalized Methods of Moments.

    Science.gov (United States)

    Caner, Mehmet; Zhang, Hao Helen

    2014-01-30

    Model selection and estimation are crucial parts of econometrics. This paper introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMM is particularly powerful for analyzing complex data sets such as longitudinal and panel data, and it has wide applications in econometrics. This paper extends the least squares based adaptive elastic net estimator of Zou and Zhang (2009) to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators lack of closed form solutions. Compared to Bridge-GMM of Caner (2009), we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables, also the redundant parameters set to zero via a data dependent technique. This method has the oracle property, meaning that we can estimate nonzero parameters with their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.

  16. Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems.

    Science.gov (United States)

    Downie, J D; Goodman, J W

    1989-10-15

    A ground-based adaptive optics imaging telescope system attempts to improve image quality by measuring and correcting for atmospherically induced wavefront aberrations. The necessary control computations during each cycle will take a finite amount of time, which adds to the residual error variance since the atmosphere continues to change during that time. Thus an optical processor may be well-suited for this task. This paper investigates this possibility by studying the accuracy requirements in a general optical processor that will make it competitive with, or superior to, a conventional digital computer for adaptive optics use.

  17. Predicting infectivity of Arbuscular Mycorrhizal fungi from soil variables using Generalized Additive Models and Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    IRNANDA AIKO FIFI DJUUNA

    2010-07-01

    Full Text Available Djuuna IAF, Abbott LK, Van Niel K (2010 Predicting infectivity of Arbuscular Mycorrhizal fungi from soil variables using Generalized Additive Models and Generalized Linear Models. Biodiversitas 11: 145-150. The objective of this study was to predict the infectivity of arbuscular mycorrhizal fungi (AM fungi, from field soil based on soil properties and land use history using generalized additive models (GAMs and generalized linear models (GLMs. A total of 291 soil samples from a farm in Western Australia near Wickepin were collected and used in this study. Nine soil properties, including elevation, pH, EC, total C, total N, P, K, microbial biomass carbon, and soil texture, and land use history of the farm were used as independent variables, while the percentage of root length colonized (%RLC was used as the dependent variable. GAMs parameterized for the percent of root length colonized suggested skewed quadratic responses to soil pH and microbial biomass carbon; cubic responses to elevation and soil K; and linear responses to soil P, EC and total C. The strength of the relationship between percent root length colonized by AM fungi and environmental variables showed that only elevation, total C and microbial biomass carbon had strong relationships. In general, GAMs and GLMs models confirmed the strong relationship between infectivity of AM fungi (assessed in a glasshouse bioassay for soil collected in summer prior to the first rain of the season and soil properties.

  18. Research of robust adaptive trajectory linearization control based on T-S fuzzy system

    Institute of Scientific and Technical Information of China (English)

    Jiang Changsheng; Zhang Chunyu; Zhu Liang

    2008-01-01

    A robust adaptive trajectory linearization control (RATLC) algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.

  19. Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

    Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

  20. Adaptive H∞ nonlinear velocity tracking using RBFNN for linear DC brushless motor

    Science.gov (United States)

    Tsai, Ching-Chih; Chan, Cheng-Kain; Li, Yi Yu

    2012-01-01

    This article presents an adaptive H ∞ nonlinear velocity control for a linear DC brushless motor. A simplified model of this motor with friction is briefly recalled. The friction dynamics is described by the Lu Gre model and the online tuning radial basis function neural network (RBFNN) is used to parameterise the nonlinear friction function and un-modelled errors. An adaptive nonlinear H ∞ control method is then proposed to achieve velocity tracking, by assuming that the upper bounds of the ripple force, the changeable load and the nonlinear friction can be learned by the RBFNN. The closed-loop system is proven to be uniformly bounded using the Lyapunov stability theory. The feasibility and the efficacy of the proposed control are exemplified by conducting two velocity tracking experiments.

  1. Generalization of visuomotor adaptation depends on the spatial characteristic of visual workspace.

    Science.gov (United States)

    Wang, Lei; Müsseler, Jochen

    2012-11-01

    The present study aims to address a novel aspect of visuomotor adaptation and its generalization. It is based on the assumption that the spatial structure of the distal action space is crucial for generalization. In the experiments, the distal action spaces could manifest either a symmetric or parallel structure. The imposed visuomotor rotations in the adaptation and the following generalization were either the same or opposing each other. In the generalization phase, motor bias resulting from prior adaptation was observed, and it turned out to substantially depend on the property of the workspace. In Experiment 1 with a parallel workspace, preceding adaptation to the same rotation was more advantageous than adaptation to an opposing rotation. This observation was reversed in Experiment 2 with the symmetrical workspace: prior adaptation to an opposing rotation was more advantageous for the generalization than prior adaptation to the same rotation. Mechanisms possibly underlying the observed influence of the workspace configuration were discussed.

  2. Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems

    Science.gov (United States)

    Downie, John D.

    1990-01-01

    A ground-based adaptive optics imaging telescope system attempts to improve image quality by detecting and correcting for atmospherically induced wavefront aberrations. The required control computations during each cycle will take a finite amount of time. Longer time delays result in larger values of residual wavefront error variance since the atmosphere continues to change during that time. Thus an optical processor may be well-suited for this task. This paper presents a study of the accuracy requirements in a general optical processor that will make it competitive with, or superior to, a conventional digital computer for the adaptive optics application. An optimization of the adaptive optics correction algorithm with respect to an optical processor's degree of accuracy is also briefly discussed.

  3. Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems

    Science.gov (United States)

    Downie, John D.

    1990-01-01

    A ground-based adaptive optics imaging telescope system attempts to improve image quality by detecting and correcting for atmospherically induced wavefront aberrations. The required control computations during each cycle will take a finite amount of time. Longer time delays result in larger values of residual wavefront error variance since the atmosphere continues to change during that time. Thus an optical processor may be well-suited for this task. This paper presents a study of the accuracy requirements in a general optical processor that will make it competitive with, or superior to, a conventional digital computer for the adaptive optics application. An optimization of the adaptive optics correction algorithm with respect to an optical processor's degree of accuracy is also briefly discussed.

  4. A speed estimation unit for induction motors based on adaptive linear combiner

    Energy Technology Data Exchange (ETDEWEB)

    Marei, Mostafa I.; Shaaban, Mostafa F.; El-Sattar, Ahmed A. [Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo 11517 (Egypt)

    2009-07-15

    This paper presents a new induction motor speed estimation technique, which can estimate the rotor resistance as well, from the measured voltage and current signals. Moreover, the paper utilizes a novel adaptive linear combiner (ADALINE) structure for speed and rotor resistance estimations. This structure can deal with the multi-output systems and it is called MO-ADALINE. The model of the induction motor is arranged in a linear form, in the stationary reference frame, to cope with the proposed speed estimator. There are many advantages of the proposed unit such as wide speed range capability, immunity against harmonics of measured waveforms, and precise estimation of the speed and the rotor resistance at different dynamic changes. Different types of induction motor drive systems are used to evaluate the dynamic performance and to examine the accuracy of the proposed unit for speed and rotor resistance estimation. (author)

  5. Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming.

    Science.gov (United States)

    García-Gen, Santiago; Rodríguez, Jorge; Lema, Juan M

    2014-12-01

    Anaerobic co-digestion of multiple substrates has the potential to enhance biogas productivity by making use of the complementary characteristics of different substrates. A blending strategy based on a linear programming optimisation method is proposed aiming at maximising COD conversion into methane, but simultaneously maintaining a digestate and biogas quality. The method incorporates experimental and heuristic information to define the objective function and the linear restrictions. The active constraints are continuously adapted (by relaxing the restriction boundaries) such that further optimisations in terms of methane productivity can be achieved. The feasibility of the blends calculated with this methodology was previously tested and accurately predicted with an ADM1-based co-digestion model. This was validated in a continuously operated pilot plant, treating for several months different mixtures of glycerine, gelatine and pig manure at organic loading rates from 1.50 to 4.93 gCOD/Ld and hydraulic retention times between 32 and 40 days at mesophilic conditions.

  6. GENERAL CENTRAL PATH AND THE LARGEST STEP GENERAL CENTRAL PATH FOLLOWING ALGORITHM FOR LINEAR PROGRAMMING

    Institute of Scientific and Technical Information of China (English)

    艾文宝; 张可村

    2001-01-01

    In this paper, we propose a general path following method, in which the starting point can be any feasible interior pair and each iteration uses a step with the largest possible reduction in duality gap. The algorithm maintains the O ( nL) ineration complexity. It enjoys quadratic convergence if the optimal vertex is nondegenerate.

  7. The Solution Structure and Error Estimation for The Generalized Linear Complementarity Problem

    Directory of Open Access Journals (Sweden)

    Tingfa Yan

    2014-07-01

    Full Text Available In this paper, we consider the generalized linear complementarity problem (GLCP. Firstly, we develop some equivalent reformulations of the problem under milder conditions, and then characterize the solution of the GLCP. Secondly, we also establish the global error estimation for the GLCP by weakening the assumption. These results obtained in this paper can be taken as an extension for the classical linear complementarity problems.

  8. Bounded Real Lemma for Generalized Linear System with Finite Discrete Jumps

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The strict bounded real lemma for linear system with finite discrete jumps was considered. Especially,the case where D matrices in the system are not assumed to be zero was dealt. Several versions of the bounded real lemma are presented in terms of solution to Riccati differential equations or inequalities with finite discrete jumps.Both the finite and infinite horizon cases are considered. These results generalize the existed bounded real lemma for linear systems.

  9. On necessity proof of strict bounded real lemma for generalized linear systems with finite discrete jumps

    Institute of Scientific and Technical Information of China (English)

    Xiaojun YANG; Zhengxin WENG; Zuohua TIAN

    2004-01-01

    Some preliminary results on strict bounded real lemma for time-varying continuous linear systems are proposed,where uncertainty in initial conditions,terminal cost and extreme of the cost function are dealt with explicitly.Based on these results,a new recursive approach is proposed in the necessity proof of strict bounded real lemma for generalized linear system with finite discrete jumps.

  10. Generalized synchronization with uncertain parameters of nonlinear dynamic system via adaptive control.

    Science.gov (United States)

    Yang, Cheng-Hsiung; Wu, Cheng-Lin

    2014-01-01

    An adaptive control scheme is developed to study the generalized adaptive chaos synchronization with uncertain chaotic parameters behavior between two identical chaotic dynamic systems. This generalized adaptive chaos synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the adaptive controller with its update laws of uncertain chaotic parameters is shown. The generalized adaptive synchronization with uncertain parameters between two identical new Lorenz-Stenflo systems is taken as three examples to show the effectiveness of the proposed method. The numerical simulations are shown to verify the results.

  11. Optimal explicit strong-stability-preserving general linear methods : complete results.

    Energy Technology Data Exchange (ETDEWEB)

    Constantinescu, E. M.; Sandu, A.; Mathematics and Computer Science; Virginia Polytechnic Inst. and State Univ.

    2009-03-03

    This paper constructs strong-stability-preserving general linear time-stepping methods that are well suited for hyperbolic PDEs discretized by the method of lines. These methods generalize both Runge-Kutta (RK) and linear multistep schemes. They have high stage orders and hence are less susceptible than RK methods to order reduction from source terms or nonhomogeneous boundary conditions. A global optimization strategy is used to find the most efficient schemes that have low storage requirements. Numerical results illustrate the theoretical findings.

  12. A generalized concordance correlation coefficient based on the variance components generalized linear mixed models for overdispersed count data.

    Science.gov (United States)

    Carrasco, Josep L

    2010-09-01

    The classical concordance correlation coefficient (CCC) to measure agreement among a set of observers assumes data to be distributed as normal and a linear relationship between the mean and the subject and observer effects. Here, the CCC is generalized to afford any distribution from the exponential family by means of the generalized linear mixed models (GLMMs) theory and applied to the case of overdispersed count data. An example of CD34+ cell count data is provided to show the applicability of the procedure. In the latter case, different CCCs are defined and applied to the data by changing the GLMM that fits the data. A simulation study is carried out to explore the behavior of the procedure with a small and moderate sample size.

  13. Analyticity of solutions of analytic non-linear general elliptic boundary value problems,and some results about linear problems

    Institute of Scientific and Technical Information of China (English)

    WANG Rouhuai

    2006-01-01

    The main aim of this paper is to discuss the problem concerning the analyticity of the solutions of analytic non-linear elliptic boundary value problems.It is proved that if the corresponding first variation is regular in Lopatinski(i) sense,then the solution is analytic up to the boundary.The method of proof really covers the case that the corresponding first variation is regularly elliptic in the sense of Douglis-Nirenberg-Volevich,and hence completely generalize the previous result of C.B.Morrey.The author also discusses linear elliptic boundary value problems for systems of ellip tic partial differential equations where the boundary operators are allowed to have singular integral operators as their coefficients.Combining the standard Fourier transform technique with analytic continuation argument,the author constructs the Poisson and Green's kernel matrices related to the problems discussed and hence obtain some representation formulae to the solutions.Some a priori estimates of Schauder type and Lp type are obtained.

  14. Adaptive PSS using a simple on-line identifier and linear pole-shift controller

    Energy Technology Data Exchange (ETDEWEB)

    Ramakrishna, G. [Department of Electrical Engineering, University of Saskatchewan, Saskatoon SK S7N 5A9 (Canada); Malik, O.P. [Department of Electrical and Computer Engineering, The University of Calgary, Calgary AB T2N 1N4 (Canada)

    2010-04-15

    Implementation of an adaptive power system stabilizer (APSS) and experimental studies are presented in this paper. The APSS consists of an adaptive linear element (ADALINE) based identifier that identifies the power system as a third-order discrete auto-regressive moving average (ARMA) model and a pole-shift controller. The ADALINE is modeled so that its weights have a one-to-one relationship with the ARMA model parameters. The weights are updated at each sampling interval to track the dynamic characteristics of the actual system. The on-line updated ARMA parameters are used in the PS control algorithm to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. The calculated control is such that it achieves regulation of the system to a constant setpoint in the shortest interval of time. Experimental studies on a physical model of power system verify that the proposed adaptive PSS effectively damps the oscillations and improves power system stability. (author)

  15. Adaptive synchronization in an array of linearly coupled neural networks with reaction-diffusion terms and time delays

    Science.gov (United States)

    Wang, Kai; Teng, Zhidong; Jiang, Haijun

    2012-10-01

    In this paper, the adaptive synchronization in an array of linearly coupled neural networks with reaction-diffusion terms and time delays is discussed. Based on the LaSalle invariant principle of functional differential equations and the adaptive feedback control technique, some sufficient conditions for adaptive synchronization of such a system are obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.

  16. Linear and nonlinear associations between general intelligence and personality in Project TALENT.

    Science.gov (United States)

    Major, Jason T; Johnson, Wendy; Deary, Ian J

    2014-04-01

    Research on the relations of personality traits to intelligence has primarily been concerned with linear associations. Yet, there are no a priori reasons why linear relations should be expected over nonlinear ones, which represent a much larger set of all possible associations. Using 2 techniques, quadratic and generalized additive models, we tested for linear and nonlinear associations of general intelligence (g) with 10 personality scales from Project TALENT (PT), a nationally representative sample of approximately 400,000 American high school students from 1960, divided into 4 grade samples (Flanagan et al., 1962). We departed from previous studies, including one with PT (Reeve, Meyer, & Bonaccio, 2006), by modeling latent quadratic effects directly, controlling the influence of the common factor in the personality scales, and assuming a direction of effect from g to personality. On the basis of the literature, we made 17 directional hypotheses for the linear and quadratic associations. Of these, 53% were supported in all 4 male grades and 58% in all 4 female grades. Quadratic associations explained substantive variance above and beyond linear effects (mean R² between 1.8% and 3.6%) for Sociability, Maturity, Vigor, and Leadership in males and Sociability, Maturity, and Tidiness in females; linear associations were predominant for other traits. We discuss how suited current theories of the personality-intelligence interface are to explain these associations, and how research on intellectually gifted samples may provide a unique way of understanding them. We conclude that nonlinear models can provide incremental detail regarding personality and intelligence associations.

  17. Interior-point algorithm based on general kernel function for monotone linear complementarity problem

    Institute of Scientific and Technical Information of China (English)

    LIU Yong; BAI Yan-qin

    2009-01-01

    A polynomial interior-point algorithm is presented for monotone linear complementarity problem (MLCP) based on:a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP.

  18. General linear methods and friends: Toward efficient solutions of multiphysics problems

    Science.gov (United States)

    Sandu, Adrian

    2017-07-01

    Time dependent multiphysics partial differential equations are of great practical importance as they model diverse phenomena that appear in mechanical and chemical engineering, aeronautics, astrophysics, meteorology and oceanography, financial modeling, environmental sciences, etc. There is no single best time discretization for the complex multiphysics systems of practical interest. We discuss "multimethod" approaches that combine different time steps and discretizations using the rigourous frameworks provided by Partitioned General Linear Methods and Generalize-structure Additive Runge Kutta Methods..

  19. A general non-linear optimization algorithm for lower bound limit analysis

    DEFF Research Database (Denmark)

    Krabbenhøft, Kristian; Damkilde, Lars

    2003-01-01

    The non-linear programming problem associated with the discrete lower bound limit analysis problem is treated by means of an algorithm where the need to linearize the yield criteria is avoided. The algorithm is an interior point method and is completely general in the sense that no particular...... finite element discretization or yield criterion is required. As with interior point methods for linear programming the number of iterations is affected only little by the problem size. Some practical implementation issues are discussed with reference to the special structure of the common lower bound...... load optimization problem. and finally the efficiency and accuracy of the method is demonstrated by means of examples of plate and slab structures obeying different non-linear yield criteria. Copyright (C) 2002 John Wiley Sons. Ltd....

  20. SELECTION OF THE LINEAR COMBINING VECTOR G OF THE GENERALIZED SELF-SHRINKING GENERATORS

    Institute of Scientific and Technical Information of China (English)

    Dong Lihua; Zeng Yong; Hu Yupu

    2006-01-01

    Given an m-sequence, the main factor influencing the least period of the Generalized Self-Shrinking (GSS) sequence is the selection of the linear combining vector G. Based on the calculation of the minimalpolynomial ofL GSS sequences and the comparison of their degrees, an algorithm for selecting the linear combining vector G is presented, which is simple to understand, to implement and to prove. By using this method,much more than 2L-1 linear combining vectors G of the desired properties will be resulted. Thus in the practical application the linear combining vector G can be chosen with great arbitrariness. Additionally, this algorithm can be extended to any finite field easily.

  1. On the distribution of discounted loss reserves using generalized linear models

    NARCIS (Netherlands)

    Hoedemakers, T.; Beirlant, J.; Goovaerts, M.J.; Dhaene, J.

    2005-01-01

    Renshaw and Verrall [11] specified the generalized linear model (GLM) underlying the chain-ladder technique and suggested some other GLMs which might be useful in claims reserving. The purpose of this paper is to construct bounds for the discounted loss reserve within the framework of GLMs. Exact

  2. Large-Sample Theory for Generalized Linear Models with Non-natural Link and Random Variates

    Institute of Scientific and Technical Information of China (English)

    Jie-li Ding; Xi-ru Chen

    2006-01-01

    For generalized linear models (GLM), in the case that the regressors are stochastic and have different distributions and the observations of the responses may have different dimensionality, the asymptotic theory of the maximum likelihood estimate (MLE) of the parameters are studied under the assumption of a non-natural link function.

  3. Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

    Science.gov (United States)

    Vidal, Sherry

    Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

  4. Asymptotic Properties of the Maximum Likelihood Estimate in Generalized Linear Models with Stochastic Regressors

    Institute of Scientific and Technical Information of China (English)

    Jie Li DING; Xi Ru CHEN

    2006-01-01

    For generalized linear models (GLM), in case the regressors are stochastic and have different distributions, the asymptotic properties of the maximum likelihood estimate (MLE)(β^)n of the parameters are studied. Under reasonable conditions, we prove the weak, strong consistency and asymptotic normality of(β^)n.

  5. The microcomputer scientific software series 2: general linear model--regression.

    Science.gov (United States)

    Harold M. Rauscher

    1983-01-01

    The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

  6. Generalized Partially Linear Regression with Misclassified Data and an Application to Labour Market Transitions

    DEFF Research Database (Denmark)

    Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

    2017-01-01

    observations from Germany. It is shown that estimated marginal effects of a number of covariates are sizeably affected by misclassification and missing values in the analysis data. The proposed generalized partially linear regression extends existing models by allowing a misclassified discrete covariate...

  7. More on Generalizations and Modifications of Iterative Methods for Solving Large Sparse Indefinite Linear Systems

    Directory of Open Access Journals (Sweden)

    Jen-Yuan Chen

    2014-01-01

    Full Text Available Continuing from the works of Li et al. (2014, Li (2007, and Kincaid et al. (2000, we present more generalizations and modifications of iterative methods for solving large sparse symmetric and nonsymmetric indefinite systems of linear equations. We discuss a variety of iterative methods such as GMRES, MGMRES, MINRES, LQ-MINRES, QR MINRES, MMINRES, MGRES, and others.

  8. Generalized Jacobi and Gauss-Seidel Methods for Solving Linear System of Equations

    Institute of Scientific and Technical Information of China (English)

    Davod Khojasteh Salkuyeh

    2007-01-01

    The Jacobi and Gauss-Seidel algorithms are among the stationary iterative methods for solving linear system of equations. They are now mostly used as preconditioners for the popular iterative solvers. In this paper a generalization of these methods are proposed and their convergence properties are studied. Some numerical experiments are given to show the efficiency of the new methods.

  9. ESTIMATION METHOD FOR SOLUTIONS TO GENERAL LINEAR SYSTEM OF VOLTERRAINTEGRAL INEQUALITIES INVOLVING ITERATED INTEGRAL FUNCTIONALS

    Institute of Scientific and Technical Information of China (English)

    MA Qinghua; YANG Enhao

    2000-01-01

    An estimation method for solutions to the general linear system of Volterratype integral inequalities containing several iterated integral functionals is obtained. This method is based on a result proved by the present second author in Journ. Math. Anal. Appl.(1984). A certain two-dimensional system of nonlinear ordinary differential equations is also discussed to demonstrate the usefulness of our method.

  10. Rate of strong consistency of quasi maximum likelihood estimate in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    [1]McCullagh, P., Nelder, J. A., Generalized Linear Models, New York: Chapman and Hall, 1989.[2]Wedderbum, R. W. M., Quasi-likelihood functions, generalized linear models and Gauss-Newton method,Biometrika, 1974, 61:439-447.[3]Fahrmeir, L., Maximum likelihood estimation in misspecified generalized linear models, Statistics, 1990, 21:487-502.[4]Fahrmeir, L., Kaufmann, H., Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models, Ann. Statist., 1985, 13: 342-368.[5]Melder, J. A., Pregibon, D., An extended quasi-likelihood function, Biometrika, 1987, 74: 221-232.[6]Bennet, G., Probability inequalities for the sum of independent random variables, JASA, 1962, 57: 33-45.[7]Stout, W. F., Almost Sure Convergence, New York:Academic Press, 1974.[8]Petrov, V, V., Sums of Independent Random Variables, Berlin, New York: Springer-Verlag, 1975.

  11. General treatment of the non-linear Rsub(Xi) gauge condition

    Energy Technology Data Exchange (ETDEWEB)

    Girardi, G.; Malleville, C.; Sorba, P. (Grenoble-1 Univ., 74 - Annecy (France). Lab. de Physique des Particules)

    1982-11-04

    It is shown that the non-linear Rsub(xi) gauge condition already introduced for the standard SU(2)xU(1) model can be generalized for any gauge model with the same type of simplification, namely the suppression of any coupling of the form: (massless gauge boson)x(massive gauge boson)x(unphysical Higgs).

  12. ON THE SOLVABILITY OF GENERAL LINEAR METHODS FOR DISSIPATIVE DYNAMICAL SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    Ai-guo Xiao

    2000-01-01

    The main purpose of the present paper is to examine the existence and local uniqueness of solutions of the implicit equations arising in the application of a weakly algebraically stable general linear methods to dissipative dynamical systems, and to extend the existing relevant results of Runge-Kutta methods by Humphries and Stuart(1994).

  13. A differential-geometric approach to generalized linear models with grouped predictors

    NARCIS (Netherlands)

    Augugliaro, Luigi; Mineo, Angelo M.; Wit, Ernst C.

    2016-01-01

    We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important

  14. Generalization of visuomotor adaptation depends on the spatial characteristic of visual workspace

    OpenAIRE

    2012-01-01

    The present study aims to address a novel aspect of visuomotor adaptation and its generalization. It is based on the assumption that the spatial structure of the distal action space is crucial for generalization. In the experiments, the distal action spaces could manifest either a symmetric or parallel structure. The imposed visuomotor rotations in the adaptation and the following generalization were either the same or opposing each other. In the generalization phase, motor bias resulting fro...

  15. Strong consistency of maximum quasi-likelihood estimates in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    YiN; Changming; ZHAO; Lincheng

    2005-01-01

    In a generalized linear model with q × 1 responses, bounded and fixed p × qregressors Zi and general link function, under the most general assumption on the mini-mum eigenvalue of∑ni=1n ZiZ'i, the moment condition on responses as weak as possibleand other mild regular conditions, we prove that with probability one, the quasi-likelihoodequation has a solutionβn for all large sample size n, which converges to the true regres-sion parameterβo. This result is an essential improvement over the relevant results in literature.

  16. Generalized model of double random phase encoding based on linear algebra

    Science.gov (United States)

    Nakano, Kazuya; Takeda, Masafumi; Suzuki, Hiroyuki; Yamaguchi, Masahiro

    2013-01-01

    We propose a generalized model for double random phase encoding (DRPE) based on linear algebra. We defined the DRPE procedure in six steps. The first three steps form an encryption procedure, while the later three steps make up a decryption procedure. We noted that the first (mapping) and second (transform) steps can be generalized. As an example of this generalization, we used 3D mapping and a transform matrix, which is a combination of a discrete cosine transform and two permutation matrices. Finally, we investigated the sensitivity of the proposed model to errors in the decryption key.

  17. Generalized linear models with random effects unified analysis via H-likelihood

    CERN Document Server

    Lee, Youngjo; Pawitan, Yudi

    2006-01-01

    Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...

  18. Adaptive Command-Filtered Backstepping Control for Linear Induction Motor via Projection Algorithm

    Directory of Open Access Journals (Sweden)

    Wenxu Yan

    2016-01-01

    Full Text Available A theoretical framework of the position control for linear induction motors (LIM has been proposed. First, indirect field-oriented control of LIM is described. Then, the backstepping approach is used to ensure the convergence and robustness of the proposed control scheme against the external time-varying disturbances via Lyapunov stability theory. At the same time, in order to solve the differential expansion and the control saturation problems in the traditional backstepping, command filter is designed in the control and compensating signals are presented to eliminate the influence of the errors caused by command filters. Next, unknown total mass of the mover, viscous friction, and load disturbances are estimated by the projection-based adaptive law which bounds the estimated function and simultaneously guarantees the robustness of the proposed controller against the parameter uncertainties. Finally, simulation results are given to illustrate the validity and potential of the designed control scheme.

  19. Position control of linear induction motor using an adaptive fuzzy integral: Back stepping controller

    Directory of Open Access Journals (Sweden)

    Bousserhane I.K.

    2006-01-01

    Full Text Available In this paper the position control of a linear induction motor using adaptive fuzzy back stepping design with integral action is proposed. First, the indirect field oriented control for LIM is derived. Then, an integral back stepping design for indirect field oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Finally, the fuzzy integral-back stepping controller is investigated, where a simple fuzzy inference mechanism is used to achieve a position tracking objective under the mechanical parameters uncertainties. The effectiveness of the proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the conventional integral back stepping control.

  20. Adaptive Digital Predistortion Schemes to Linearize RF Power Amplifiers with Memory Effects

    Institute of Scientific and Technical Information of China (English)

    ZHANG Peng; WU Si-liang; ZHANG Qin

    2008-01-01

    To compensate for nonlinear distortion introduced by RF power amplifiers (PAs) with memory effects, two correlated models, namely an extended memory polynomial (EMP) model and a memory lookup table (LUT) model, are proposed for predistorter design. Two adaptive digital predistortion (ADPD) schemes with indirect learning architecture are presented. One adopts the EMP model and the recursive least square (RLS) algorithm, and the other utilizes the memory LUT model and the least mean square (LMS) algorithm. Simulation results demonstrate that the EMP-based ADPD yields the best linearization performance in terms of suppressing spectral regrowth. It is also shown that the ADPD based on memory LUT makes optimum tradeoff between performance and computational complexity.

  1. Missing pixels restoration for remote sensing images using adaptive search window and linear regression

    Science.gov (United States)

    Tai, Shen-Chuan; Chen, Peng-Yu; Chao, Chian-Yen

    2016-07-01

    The Consultative Committee for Space Data Systems proposed an efficient image compression standard that can do lossless compression (CCSDS-ICS). CCSDS-ICS is the most widely utilized standard for satellite communications. However, the original CCSDS-ICS is weak in terms of error resilience with even a single incorrect bit possibly causing numerous missing pixels. A restoration algorithm based on the neighborhood similar pixel interpolator is proposed to fill in missing pixels. The linear regression model is used to generate the reference image from other panchromatic or multispectral images. Furthermore, an adaptive search window is utilized to sieve out similar pixels from the pixels in the search region defined in the neighborhood similar pixel interpolator. The experimental results show that the proposed methods are capable of reconstructing missing regions with good visual quality.

  2. Consensus of Continuous-Time Multiagent Systems with General Linear Dynamics and Nonuniform Sampling

    Directory of Open Access Journals (Sweden)

    Yanping Gao

    2013-01-01

    Full Text Available This paper studies the consensus problem of multiple agents with general linear continuous-time dynamics. It is assumed that the information transmission among agents is intermittent; namely, each agent can only obtain the information of other agents at some discrete times, where the discrete time intervals may not be equal. Some sufficient conditions for consensus in the cases of state feedback and static output feedback are established, and it is shown that if the controller gain and the upper bound of discrete time intervals satisfy certain linear matrix inequality, then consensus can be reached. Simulations are performed to validate the theoretical results.

  3. H∞ filtering of Markov jump linear systems with general transition probabilities and output quantization.

    Science.gov (United States)

    Shen, Mouquan; Park, Ju H

    2016-07-01

    This paper addresses the H∞ filtering of continuous Markov jump linear systems with general transition probabilities and output quantization. S-procedure is employed to handle the adverse influence of the quantization and a new approach is developed to conquer the nonlinearity induced by uncertain and unknown transition probabilities. Then, sufficient conditions are presented to ensure the filtering error system to be stochastically stable with the prescribed performance requirement. Without specified structure imposed on introduced slack variables, a flexible filter design method is established in terms of linear matrix inequalities. The effectiveness of the proposed method is validated by a numerical example.

  4. Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces.

    Science.gov (United States)

    Nicolas-Alonso, Luis F; Corralejo, Rebeca; Gomez-Pilar, Javier; Álvarez, Daniel; Hornero, Roberto

    2015-07-01

    Practical motor imagery-based brain computer interface (MI-BCI) applications are limited by the difficult to decode brain signals in a reliable way. In this paper, we propose a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks. Stacked generalization is used to exploit the power of classifier ensembles for combining information coming from multiple sources and reducing the existing uncertainty in EEG signals. The outputs of several regularized linear discriminant analysis (RLDA) models are combined to account for temporal, spatial, and spectral information. The resultant algorithm is called stacked RLDA (SRLDA). Additionally, an adaptive processing stage is introduced before classification to reduce the harmful effect of intersession non-stationarity. The benefits of the proposed method are evaluated on the BCI Competition IV dataset 2a. We demonstrate its effectiveness in binary and multiclass settings with four different motor imagery tasks: left-hand, right-hand, both feet, and tongue movements. The results show that adaptive SRLDA outperforms the winner of the competition and other approaches tested on this multiclass dataset.

  5. Novel Adaptive Learning Control of Linear Systems with Completely Unknown Time Delays

    Institute of Scientific and Technical Information of China (English)

    Wei-Sheng Chen

    2009-01-01

    A novel output-feedback adaptive learning control approach is developed for a class of linear time-delay systems. Three kinds of uncertainties: time delays, number of time delays, and system parameters are all assumed to be completely unknown, which is different from the previous work. The design procedure includes two steps. First, according to the given periodic desired reference output and the allowed bound of tracking error, a suitable finite Fourier series expansion (FSE) is chosen as a practical reference output to he tracked. Second, by expressing the delayed practical reference output as a known time-varying vector multiplied by an unknown constant vector, we combine three kinds of uncertainties into an unknown constant vector and then estimate the vector by designing an adaptive law. By constructing a Lyapunov-Krasovskii functional, it is proved that the system output can asymptotically track the practical reference signal An example is provided to illustrate the effectiveness of the control scheme developed in this paper.

  6. Adapting Predictive Models for Cepheid Variable Star Classification Using Linear Regression and Maximum Likelihood

    Science.gov (United States)

    Gupta, Kinjal Dhar; Vilalta, Ricardo; Asadourian, Vicken; Macri, Lucas

    2014-05-01

    We describe an approach to automate the classification of Cepheid variable stars into two subtypes according to their pulsation mode. Automating such classification is relevant to obtain a precise determination of distances to nearby galaxies, which in addition helps reduce the uncertainty in the current expansion of the universe. One main difficulty lies in the compatibility of models trained using different galaxy datasets; a model trained using a training dataset may be ineffectual on a testing set. A solution to such difficulty is to adapt predictive models across domains; this is necessary when the training and testing sets do not follow the same distribution. The gist of our methodology is to train a predictive model on a nearby galaxy (e.g., Large Magellanic Cloud), followed by a model-adaptation step to make the model operable on other nearby galaxies. We follow a parametric approach to density estimation by modeling the training data (anchor galaxy) using a mixture of linear models. We then use maximum likelihood to compute the right amount of variable displacement, until the testing data closely overlaps the training data. At that point, the model can be directly used in the testing data (target galaxy).

  7. Adaptive band-limited disturbance rejection in linear discrete-time systems

    Directory of Open Access Journals (Sweden)

    Foued Ben-Amara

    1995-01-01

    Full Text Available The problem of adaptively rejecting a disturbance consisting of a linear combination of sinusoids with unknown and/or time varying frequencies for SISO LTI discrete-time systems is considered. The rejection of the disturbance input is achieved by constructing the set of stabilizing controllers using the Youla parametrization and adjusting the Youla parameter to achieve asymptotic disturbance rejection. The first main result in this paper concerns off-line controller design where a controller that achieves regulation is numerically designed off-line based on the assumption that only the sequence of discrete disturbance input values (as opposed to a model of the disturbance is available. A least squares based optimization algorithm is used in the controller design. As expected, it is shown, under some mild assumptions, that if the off-line designed controller achieves regulation, then it must include a model of the disturbance input. The second main result concerns on-line controller design where recursive versions of the off-line algorithm used above for controller design are presented and their convergence properties analyzed. Conditions under which the on-line algorithms yield an asymptotic controller that achieves regulation are presented. Conditions both for the case where the disturbance input properties are constant but unknown and for the case where they are unknown and time-varying are given. The on-line controller construction amounts to an adaptive implementation of the Internal Model Principle. The performance robustness of the off-line designed controller in the face of plant model uncertainties is investigated. It is shown, under some mild assumptions, that performance robustness is realized provided internal stability is maintained. The performance of the adaptation algorithms is illustrated through a simulation example.

  8. Linearity enhancement of TVGA based on adaptive sweep optimisation in monostatic radar receiver

    Science.gov (United States)

    Almslmany, Amir; Wang, Caiyun; Cao, Qunsheng

    2016-08-01

    The limited input dynamic power range of the radar receiver and the power loss due to the targets' ranges are two potential problems in the radar receivers. This paper proposes a model based on the time-varying gain amplifier (TVGA) to compensate the power loss from the targets' ranges, and using the negative impedance compensation technique to enhance the TVGA linearity based on Volterra series. The simulation has been done based on adaptive sweep optimisation (ASO) using advanced design system (ADS) and Matlab. It shows that the suppression of the third-order intermodulation products (IMR3) was carried out for two-tone test, the high-gain accuracy improved by 3 dB, and the high linearity IMR3 improved by 14 dB. The monostatic radar system was tested to detect three targets at different ranges and to compare its probability of detection with the prior models; the results show that the probability of detection has been increased for ASO/TVGA.

  9. Hierarchical Shrinkage Priors and Model Fitting for High-dimensional Generalized Linear Models

    Science.gov (United States)

    Yi, Nengjun; Ma, Shuangge

    2013-01-01

    Genetic and other scientific studies routinely generate very many predictor variables, which can be naturally grouped, with predictors in the same groups being highly correlated. It is desirable to incorporate the hierarchical structure of the predictor variables into generalized linear models for simultaneous variable selection and coefficient estimation. We propose two prior distributions: hierarchical Cauchy and double-exponential distributions, on coefficients in generalized linear models. The hierarchical priors include both variable-specific and group-specific tuning parameters, thereby not only adopting different shrinkage for different coefficients and different groups but also providing a way to pool the information within groups. We fit generalized linear models with the proposed hierarchical priors by incorporating flexible expectation-maximization (EM) algorithms into the standard iteratively weighted least squares as implemented in the general statistical package R. The methods are illustrated with data from an experiment to identify genetic polymorphisms for survival of mice following infection with Listeria monocytogenes. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). PMID:23192052

  10. A TRUST REGION ALGORITHM VIA BILEVEL LINEAR PROGRAMMING FOR SOLVING THE GENERAL MULTICOMMODITY MINIMAL COST FLOW PROBLEMS

    Institute of Scientific and Technical Information of China (English)

    ZhuDetong

    2004-01-01

    This paper proposes a nonmonotonic backtracking trust region algorithm via bilevel linear programming for solving the general multicommodity minimal cost flow problems. Using the duality theory of the linear programming and convex theory, the generalized directional derivative of the general multicommodity minimal cost flow problems is derived. The global convergence and superlinear convergence rate of the proposed algorithm are established under some mild conditions.

  11. An Optimally Generalized Steepest-Descent Algorithm for Solving Ill-Posed Linear Systems

    Directory of Open Access Journals (Sweden)

    Chein-Shan Liu

    2013-01-01

    Full Text Available It is known that the steepest-descent method converges normally at the first few iterations, and then it slows down. We modify the original steplength and descent direction by an optimization argument with the new steplength as being a merit function to be maximized. An optimal iterative algorithm with m-vector descent direction in a Krylov subspace is constructed, of which the m optimal weighting parameters are solved in closed-form to accelerate the convergence speed in solving ill-posed linear problems. The optimally generalized steepest-descent algorithm (OGSDA is proven to be convergent with very fast convergence speed, accurate and robust against noisy disturbance, which is confirmed by numerical tests of some well-known ill-posed linear problems and linear inverse problems.

  12. General relativistic hydrodynamics with Adaptive-Mesh Refinement (AMR) and modeling of accretion disks

    Science.gov (United States)

    Donmez, Orhan

    We present a general procedure to solve the General Relativistic Hydrodynamical (GRH) equations with Adaptive-Mesh Refinement (AMR) and model of an accretion disk around a black hole. To do this, the GRH equations are written in a conservative form to exploit their hyperbolic character. The numerical solutions of the general relativistic hydrodynamic equations is done by High Resolution Shock Capturing schemes (HRSC), specifically designed to solve non-linear hyperbolic systems of conservation laws. These schemes depend on the characteristic information of the system. We use Marquina fluxes with MUSCL left and right states to solve GRH equations. First, we carry out different test problems with uniform and AMR grids on the special relativistic hydrodynamics equations to verify the second order convergence of the code in 1D, 2 D and 3D. Second, we solve the GRH equations and use the general relativistic test problems to compare the numerical solutions with analytic ones. In order to this, we couple the flux part of general relativistic hydrodynamic equation with a source part using Strang splitting. The coupling of the GRH equations is carried out in a treatment which gives second order accurate solutions in space and time. The test problems examined include shock tubes, geodesic flows, and circular motion of particle around the black hole. Finally, we apply this code to the accretion disk problems around the black hole using the Schwarzschild metric at the background of the computational domain. We find spiral shocks on the accretion disk. They are observationally expected results. We also examine the star-disk interaction near a massive black hole. We find that when stars are grounded down or a hole is punched on the accretion disk, they create shock waves which destroy the accretion disk.

  13. General job stress: a unidimensional measure and its non-linear relations with outcome variables.

    Science.gov (United States)

    Yankelevich, Maya; Broadfoot, Alison; Gillespie, Jennifer Z; Gillespie, Michael A; Guidroz, Ashley

    2012-04-01

    This article aims to examine the non-linear relations between a general measure of job stress [Stress in General (SIG)] and two outcome variables: intentions to quit and job satisfaction. In so doing, we also re-examine the factor structure of the SIG and determine that, as a two-factor scale, it obscures non-linear relations with outcomes. Thus, in this research, we not only test for non-linear relations between stress and outcome variables but also present an updated version of the SIG scale. Using two distinct samples of working adults (sample 1, N = 589; sample 2, N = 4322), results indicate that a more parsimonious eight-item SIG has better model-data fit than the 15-item two-factor SIG and that the eight-item SIG has non-linear relations with job satisfaction and intentions to quit. Specifically, the revised SIG has an inverted curvilinear J-shaped relation with job satisfaction such that job satisfaction drops precipitously after a certain level of stress; the SIG has a J-shaped curvilinear relation with intentions to quit such that turnover intentions increase exponentially after a certain level of stress.

  14. A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

    KAUST Repository

    Huang, Jianhua Z.

    2012-03-01

    Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.

  15. A review of linear response theory for general differentiable dynamical systems

    Science.gov (United States)

    Ruelle, David

    2009-04-01

    The classical theory of linear response applies to statistical mechanics close to equilibrium. Away from equilibrium, one may describe the microscopic time evolution by a general differentiable dynamical system, identify nonequilibrium steady states (NESS) and study how these vary under perturbations of the dynamics. Remarkably, it turns out that for uniformly hyperbolic dynamical systems (those satisfying the 'chaotic hypothesis'), the linear response away from equilibrium is very similar to the linear response close to equilibrium: the Kramers-Kronig dispersion relations hold, and the fluctuation-dispersion theorem survives in a modified form (which takes into account the oscillations around the 'attractor' corresponding to the NESS). If the chaotic hypothesis does not hold, two new phenomena may arise. The first is a violation of linear response in the sense that the NESS does not depend differentiably on parameters (but this nondifferentiability may be hard to see experimentally). The second phenomenon is a violation of the dispersion relations: the susceptibility has singularities in the upper half complex plane. These 'acausal' singularities are actually due to 'energy nonconservation': for a small periodic perturbation of the system, the amplitude of the linear response is arbitrarily large. This means that the NESS of the dynamical system under study is not 'inert' but can give energy to the outside world. An 'active' NESS of this sort is very different from an equilibrium state, and it would be interesting to see what happens for active states to the Gallavotti-Cohen fluctuation theorem.

  16. Convergence analysis for general linear methods applied to stiff delay differential equations

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    For Runge-Kutta methods applied to stiff delay differential equations (DDEs), the concept of D-convergence was proposed, which is an extension to that of B-convergence in ordinary differential equations (ODEs). In this paper, D-convergence of general linear methods is discussed and the previous related results are improved. Some order results to determine D-convergence of the methods are obtained.

  17. Bayesian prediction of spatial count data using generalized linear mixed models

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge

    2002-01-01

    Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we...... demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction....

  18. ASYMPTOTIC NORMALITY OF QUASI MAXIMUM LIKELIHOOD ESTIMATE IN GENERALIZED LINEAR MODELS

    Institute of Scientific and Technical Information of China (English)

    YUE LI; CHEN XIRU

    2005-01-01

    For the Generalized Linear Model (GLM), under some conditions including that the specification of the expectation is correct, it is shown that the Quasi Maximum Likelihood Estimate (QMLE) of the parameter-vector is asymptotic normal. It is also shown that the asymptotic covariance matrix of the QMLE reaches its minimum (in the positive-definte sense) in case that the specification of the covariance matrix is correct.

  19. Damping of a system of linear oscillators using the generalized dry friction

    OpenAIRE

    Ovseevich, Alexander; Fedorov, Aleksey

    2015-01-01

    The problem of damping a system of linear oscillators is considered. The problem is solved by using a control in the form of dry friction. The motion of the system under the control is governed by a system of differential equations with discontinuous right-hand side. A uniqueness and continuity theorem is proved for the phase flow of this system. Thus, the control in the form of generalized dry friction defines the motion of the system of oscillators uniquely.

  20. Representations of general linear groups and categorical actions of Kac-Moody algebras

    OpenAIRE

    Losev, Ivan

    2012-01-01

    This is an expanded version of the lectures given by the author on the 3rd school "Lie algebras, algebraic groups and invariant theory" in Togliatti, Russia. In these notes we explain the concept of a categorical Kac-Moody action by studying an example of the category of rational representations of a general linear group in positive characteristic. We also deal with some more advanced topics: a categorical action on the polynomial representations and crystals of categorical actions.

  1. An Entropy-Based Approach to Path Analysis of Structural Generalized Linear Models: A Basic Idea

    Directory of Open Access Journals (Sweden)

    Nobuoki Eshima

    2015-07-01

    Full Text Available A path analysis method for causal systems based on generalized linear models is proposed by using entropy. A practical example is introduced, and a brief explanation of the entropy coefficient of determination is given. Direct and indirect effects of explanatory variables are discussed as log odds ratios, i.e., relative information, and a method for summarizing the effects is proposed. The example dataset is re-analyzed by using the method.

  2. An Average Linear Difference Scheme for the Generalized Rosenau-KdV Equation

    Directory of Open Access Journals (Sweden)

    Maobo Zheng

    2014-01-01

    Full Text Available An average linear finite difference scheme for the numerical solution of the initial-boundary value problem of Generalized Rosenau-KdV equation is proposed. The existence, uniqueness, and conservation for energy of the difference solution are proved by the discrete energy norm method. It is shown that the finite difference scheme is 2nd-order convergent and unconditionally stable. Numerical experiments verify that the theoretical results are right and the numerical method is efficient and reliable.

  3. Solution to the Generalized Champagne Problem on simultaneous stabilization of linear systems

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The well-known Generalized Champagne Problem on simultaneous stabilization of linear systems is solved by using complex analysis and Blondel's technique. We give a complete answer to the open problem proposed by Patel et al., which automatically includes the solution to the original Champagne Problem. Based on the recent development in automated inequality-type theorem proving, a new stabilizing controller design method is established. Our numerical examples significantly improve the relevant results in the literature.

  4. Adaptive Finite Element Modeling of Marine Controlled-Source Electromagnetic Fields in Two-Dimensional General Anisotropic Media

    Institute of Scientific and Technical Information of China (English)

    LI Yuguo; LUO Ming; PEI Jianxin

    2013-01-01

    In this paper,we extend the scope of numerical simulations of marine controlled-source electromagnetic (CSEM) fields in a particular case of anisotropy (dipping anisotropy) to the general case of anisotropy by using an adaptive finite element approach.In comparison to a dipping anisotropy case,the first order spatial derivatives of the strike-parallel components arise in the partial differential equations for generally anisotropic media,which cause a non-symmetric linear system of equations for finite element modeling.The adaptive finite element method is employed to obtain numerical solutions on a sequence of refined unstructured triangular meshes,which allows for arbitrary model geometries including bathymetry and dipping layers.Numerical results of a 2D anisotropic model show both anisotropy strike and dipping angles have great influence on the marine CSEM responses.

  5. Second degree generalized Jacobi iteration method for solving system of linear equations

    Directory of Open Access Journals (Sweden)

    Tesfaye Kebede Enyew

    2016-05-01

    Full Text Available In this paper, a Second degree generalized Jacobi Iteration method for solving system of linear equations, $Ax=b$ and discuss about the optimal values $a_{1}$ and $b_{1}$ in terms of spectral radius about for the convergence of SDGJ method of $x^{(n+1}=b_{1}[D_{m}^{-1}(L_{m}+U_{m}x^{(n}+k_{1m}]-a_{1}x^{(n-1}.$ Few numerical examples are considered to show that the effective of the Second degree Generalized Jacobi Iteration method (SDGJ in comparison with FDJ, FDGJ, SDJ.

  6. LINEAR LAYER AND GENERALIZED REGRESSION COMPUTATIONAL INTELLIGENCE MODELS FOR PREDICTING SHELF LIFE OF PROCESSED CHEESE

    Directory of Open Access Journals (Sweden)

    S. Goyal

    2012-03-01

    Full Text Available This paper highlights the significance of computational intelligence models for predicting shelf life of processed cheese stored at 7-8 g.C. Linear Layer and Generalized Regression models were developed with input parameters: Soluble nitrogen, pH, Standard plate count, Yeast & mould count, Spores, and sensory score as output parameter. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the models. The study revealed that Generalized Regression computational intelligence models are quite effective in predicting the shelf life of processed cheese stored at 7-8 g.C.

  7. Scheme for purifying a general mixed entangled state and its linear optical implementation

    Institute of Scientific and Technical Information of China (English)

    董冬; 张延磊; 邹长铃; 邹旭波; 郭光灿

    2015-01-01

    We propose a scheme for purification of a general mixed entangled state. In this scheme, we start from a large number of general mixed entangled states and end up, after local operation and classical communication, with a smaller number of Bell diagonal states with higher entanglement. In particular, the scheme can purify one maximally entangled state from two entangled pairs prepared in a class of mixed entangled state. Furthermore we propose a linear optical implementation of the present scheme with polarization beam splitters and photon detectors.

  8. Interactions in Generalized Linear Models: Theoretical Issues and an Application to Personal Vote-Earning Attributes

    Directory of Open Access Journals (Sweden)

    Tsung-han Tsai

    2013-05-01

    Full Text Available There is some confusion in political science, and the social sciences in general, about the meaning and interpretation of interaction effects in models with non-interval, non-normal outcome variables. Often these terms are casually thrown into a model specification without observing that their presence fundamentally changes the interpretation of the resulting coefficients. This article explains the conditional nature of reported coefficients in models with interactions, defining the necessarily different interpretation required by generalized linear models. Methodological issues are illustrated with an application to voter information structured by electoral systems and resulting legislative behavior and democratic representation in comparative politics.

  9. Invariance of the generalized oscillator under a linear transformation of the related system of orthogonal polynomials

    Science.gov (United States)

    Borzov, V. V.; Damaskinsky, E. V.

    2017-02-01

    We consider the families of polynomials P = { P n ( x)} n=0 ∞ and Q = { Q n ( x)} n=0 ∞ orthogonal on the real line with respect to the respective probability measures μ and ν. We assume that { Q n ( x)} n=0 ∞ and { P n ( x)} n=0 ∞ are connected by linear relations. In the case k = 2, we describe all pairs (P,Q) for which the algebras A P and A Q of generalized oscillators generated by { Qn(x)} n=0 ∞ and { Pn(x)} n=0 ∞ coincide. We construct generalized oscillators corresponding to pairs (P,Q) for arbitrary k ≥ 1.

  10. Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

    Directory of Open Access Journals (Sweden)

    Mingwu Jin

    2012-01-01

    Full Text Available Local canonical correlation analysis (CCA is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM, a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

  11. Linear and non-linear heart rate metrics for the assessment of anaesthetists' workload during general anaesthesia.

    Science.gov (United States)

    Martin, J; Schneider, F; Kowalewskij, A; Jordan, D; Hapfelmeier, A; Kochs, E F; Wagner, K J; Schulz, C M

    2016-12-01

    Excessive workload may impact the anaesthetists' ability to adequately process information during clinical practice in the operation room and may result in inaccurate situational awareness and performance. This exploratory study investigated heart rate (HR), linear and non-linear heart rate variability (HRV) metrics and subjective ratings scales for the assessment of workload associated with the anaesthesia stages induction, maintenance and emergence. HR and HRV metrics were calculated based on five min segments from each of the three anaesthesia stages. The area under the receiver operating characteristics curve (AUC) of the investigated metrics was calculated to assess their ability to discriminate between the stages of anaesthesia. Additionally, a multiparametric approach based on logistic regression models was performed to further evaluate whether linear or non-linear heart rate metrics are suitable for the assessment of workload. Mean HR and several linear and non-linear HRV metrics including subjective workload ratings differed significantly between stages of anaesthesia. Permutation Entropy (PeEn, AUC=0.828) and mean HR (AUC=0.826) discriminated best between the anaesthesia stages induction and maintenance. In the multiparametric approach using logistic regression models, the model based on non-linear heart rate metrics provided a higher AUC compared with the models based on linear metrics. In this exploratory study based on short ECG segment analysis, PeEn and HR seem to be promising to separate workload levels between different stages of anaesthesia. The multiparametric analysis of the regression models favours non-linear heart rate metrics over linear metrics. © The Author 2016. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. Linear matrix inequality-based nonlinear adaptive robust control with application to unmanned aircraft systems

    Science.gov (United States)

    Kun, David William

    Unmanned aircraft systems (UASs) are gaining popularity in civil and commercial applications as their lightweight on-board computers become more powerful and affordable, their power storage devices improve, and the Federal Aviation Administration addresses the legal and safety concerns of integrating UASs in the national airspace. Consequently, many researchers are pursuing novel methods to control UASs in order to improve their capabilities, dependability, and safety assurance. The nonlinear control approach is a common choice as it offers several benefits for these highly nonlinear aerospace systems (e.g., the quadrotor). First, the controller design is physically intuitive and is derived from well known dynamic equations. Second, the final control law is valid in a larger region of operation, including far from the equilibrium states. And third, the procedure is largely methodical, requiring less expertise with gain tuning, which can be arduous for a novice engineer. Considering these facts, this thesis proposes a nonlinear controller design method that combines the advantages of adaptive robust control (ARC) with the powerful design tools of linear matrix inequalities (LMI). The ARC-LMI controller is designed with a discontinuous projection-based adaptation law, and guarantees a prescribed transient and steady state tracking performance for uncertain systems in the presence of matched disturbances. The norm of the tracking error is bounded by a known function that depends on the controller design parameters in a known form. Furthermore, the LMI-based part of the controller ensures the stability of the system while overcoming polytopic uncertainties, and minimizes the control effort. This can reduce the number of parameters that require adaptation, and helps to avoid control input saturation. These desirable characteristics make the ARC-LMI control algorithm well suited for the quadrotor UAS, which may have unknown parameters and may encounter external

  13. Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens.

    Science.gov (United States)

    Métris, Aline; George, Susie M; Ropers, Delphine

    2017-01-02

    Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available

  14. Generalized Distributed Network Coding Based on Nonbinary Linear Block Codes for Multi-User Cooperative Communications

    CERN Document Server

    Rebelatto, João Luiz; Li, Yonghui; Vucetic, Branka

    2010-01-01

    In this work, we propose and analyze a generalized construction of distributed network codes for a network consisting of M users sending different information to a common base station through independent block fading channels. The aim is to increase the diversity order of the system without reducing its code rate. The proposed scheme, called generalized dynamic network codes (GDNC), is a generalization of the dynamic network codes (DNC) recently proposed by Xiao and Skoglung. The design of the network codes that maximizes the diversity order is recognized as equivalent to the design of linear block codes over a nonbinary finite field under the Hamming metric. The proposed scheme offers a much better tradeoff between rate and diversity order. An outage probability analysis showing the improved performance is carried out, and computer simulations results are shown to agree with the analytical results.

  15. Bayesian Variable Selection and Computation for Generalized Linear Models with Conjugate Priors.

    Science.gov (United States)

    Chen, Ming-Hui; Huang, Lan; Ibrahim, Joseph G; Kim, Sungduk

    2008-07-01

    In this paper, we consider theoretical and computational connections between six popular methods for variable subset selection in generalized linear models (GLM's). Under the conjugate priors developed by Chen and Ibrahim (2003) for the generalized linear model, we obtain closed form analytic relationships between the Bayes factor (posterior model probability), the Conditional Predictive Ordinate (CPO), the L measure, the Deviance Information Criterion (DIC), the Aikiake Information Criterion (AIC), and the Bayesian Information Criterion (BIC) in the case of the linear model. Moreover, we examine computational relationships in the model space for these Bayesian methods for an arbitrary GLM under conjugate priors as well as examine the performance of the conjugate priors of Chen and Ibrahim (2003) in Bayesian variable selection. Specifically, we show that once Markov chain Monte Carlo (MCMC) samples are obtained from the full model, the four Bayesian criteria can be simultaneously computed for all possible subset models in the model space. We illustrate our new methodology with a simulation study and a real dataset.

  16. Normality of raw data in general linear models: The most widespread myth in statistics

    Science.gov (United States)

    Kery, Marc; Hatfield, Jeff S.

    2003-01-01

    In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

  17. The potential in general linear electrodynamics. Causal structure, propagators and quantization

    Energy Technology Data Exchange (ETDEWEB)

    Siemssen, Daniel [Department of Mathematical Methods in Physics, Faculty of Physics, University of Warsaw (Poland); Pfeifer, Christian [Institute for Theoretical Physics, Leibniz Universitaet Hannover (Germany); Center of Applied Space Technology and Microgravity (ZARM), Universitaet Bremen (Germany)

    2016-07-01

    From an axiomatic point of view, the fundamental input for a theory of electrodynamics are Maxwell's equations dF=0 (or F=dA) and dH=J, and a constitutive law H=F, which relates the field strength 2-form F and the excitation 2-form H. In this talk we consider general linear electrodynamics, the theory of electrodynamics defined by a linear constitutive law. The best known application of this theory is the effective description of electrodynamics inside (linear) media (e.g. birefringence). We analyze the classical theory of the electromagnetic potential A before we use methods familiar from mathematical quantum field theory in curved spacetimes to quantize it. Our analysis of the classical theory contains the derivation of retarded and advanced propagators, the analysis of the causal structure on the basis of the constitutive law (instead of a metric) and a discussion of the classical phase space. This classical analysis sets the stage for the construction of the quantum field algebra and quantum states, including a (generalized) microlocal spectrum condition.

  18. A general theory of linear cosmological perturbations: scalar-tensor and vector-tensor theories

    CERN Document Server

    Lagos, Macarena; Ferreira, Pedro G; Noller, Johannes

    2016-01-01

    We present a method for parametrizing linear cosmological perturbations of theories of gravity, around homogeneous and isotropic backgrounds. The method is sufficiently general and systematic that it can be applied to theories with any degrees of freedom (DoFs) and arbitrary gauge symmetries. In this paper, we focus on scalar-tensor and vector-tensor theories, invariant under linear coordinate transformations. In the case of scalar-tensor theories, we use our framework to recover the simple parametrizations of linearized Horndeski and "Beyond Horndeski" theories, and also find higher-derivative corrections. In the case of vector-tensor theories, we first construct the most general quadratic action for perturbations that leads to second-order equations of motion, which propagates two scalar DoFs. Then we specialize to the case in which the vector field is time-like (\\`a la Einstein-Aether gravity), where the theory only propagates one scalar DoF. As a result, we identify the complete forms of the quadratic act...

  19. Adaptation of a general circulation model to ocean dynamics

    Science.gov (United States)

    Turner, R. E.; Rees, T. H.; Woodbury, G. E.

    1976-01-01

    A primitive-variable general circulation model of the ocean was formulated in which fast external gravity waves are suppressed with rigid-lid surface constraint pressires which also provide a means for simulating the effects of large-scale free-surface topography. The surface pressure method is simpler to apply than the conventional stream function models, and the resulting model can be applied to both global ocean and limited region situations. Strengths and weaknesses of the model are also presented.

  20. Automated Clutch of AMT Vehicle Based on Adaptive Generalized Minimum Variance Controller

    National Research Council Canada - National Science Library

    Ze Li; Xinhao Yang

    2014-01-01

    ... of the automated clutch of automatic mechanical transmission vehicle. In this paper, an adaptive generalized minimum variance controller is applied to the automated clutch, which is driven by a brushless DC motor...

  1. Robust Adaptive Fuzzy Design for Ship Linear-tracking Control with Input Saturation

    Directory of Open Access Journals (Sweden)

    Yancai Hu

    2017-04-01

    Full Text Available A robust adaptive control approach is proposed for underactuated surface ship linear path-tracking control system based on the backstepping control method and Lyapunov stability theory. By employing T-S fuzzy system to approximate nonlinear uncertainties of the control system, the proposed scheme is developed by combining “dynamic surface control” (DSC and “minimal learning parameter” (MLP techniques. The substantial problems of “explosion of complexity” and “dimension curse” existed in the traditional backstepping technique are circumvented, and it is convenient to implement in applications. In addition, an auxiliary system is developed to deal with the effect of input saturation constraints. The control algorithm avoids the singularity problem of controller and guarantees the stability of the closed-loop system. The tracking error converges to an arbitrarily small neighborhood. Finally, MATLAB simulation results are given from an application case of Dalian Maritime University training ship to demonstrate the effectiveness of the proposed scheme.

  2. Adaptive tracking control of leader-following linear multi-agent systems with external disturbances

    Science.gov (United States)

    Lin, Hanquan; Wei, Qinglai; Liu, Derong; Ma, Hongwen

    2016-10-01

    In this paper, the consensus problem for leader-following linear multi-agent systems with external disturbances is investigated. Brownian motions are used to describe exogenous disturbances. A distributed tracking controller based on Riccati inequalities with an adaptive law for adjusting coupling weights between neighbouring agents is designed for leader-following multi-agent systems under fixed and switching topologies. In traditional distributed static controllers, the coupling weights depend on the communication graph. However, coupling weights associated with the feedback gain matrix in our method are updated by state errors between neighbouring agents. We further present the stability analysis of leader-following multi-agent systems with stochastic disturbances under switching topology. Most traditional literature requires the graph to be connected all the time, while the communication graph is only assumed to be jointly connected in this paper. The design technique is based on Riccati inequalities and algebraic graph theory. Finally, simulations are given to show the validity of our method.

  3. Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

    Directory of Open Access Journals (Sweden)

    Muhammad Ammirrul Atiqi Mohd Zainuri

    2016-05-01

    Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.

  4. Generalization of Hindi OCR Using Adaptive Segmentation and Font Files

    Science.gov (United States)

    Agrawal, Mudit; Ma, Huanfeng; Doermann, David

    In this chapter, we describe an adaptive Indic OCR system implemented as part of a rapidly retargetable language tool effort and extend work found in [20, 2]. The system includes script identification, character segmentation, training sample creation, and character recognition. For script identification, Hindi words are identified in bilingual or multilingual document images using features of the Devanagari script and support vector machine (SVM). Identified words are then segmented into individual characters, using a font-model-based intelligent character segmentation and recognition system. Using characteristics of structurally similar TrueType fonts, our system automatically builds a model to be used for the segmentation and recognition of the new script, independent of glyph composition. The key is a reliance on known font attributes. In our recognition system three feature extraction methods are used to demonstrate the importance of appropriate features for classification. The methods are tested on both Latin and non-Latin scripts. Results show that the character-level recognition accuracy exceeds 92% for non-Latin and 96% for Latin text on degraded documents. This work is a step toward the recognition of scripts of low-density languages which typically do not warrant the development of commercial OCR, yet often have complete TrueType font descriptions.

  5. The quantum general linear supergroup, canonical bases and Kazhdan-Lusztig polynomials

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Canonical bases of the tensor powers of the natural Uq(glm|n)-module V are constructed by adapting the work of Frenkel, Khovanov and Kirrilov to the quantum supergroup setting. This result is generalized in several directions. We first construct the canonical bases of the Z2-graded symmetric algebra of V and tensor powers of this superalgebra; then construct canonical bases for the superalgebra Oq(Mm|n) of a quantum (m, n) × (m, n)-supermatrix; and finally deduce from the latter result the canonical basis of every irreducible tensor module for Uq(glm|n) by applying a quantum analogue of the Borel-Weil construction.

  6. Generalized linear sampling method for elastic-wave sensing of heterogeneous fractures

    CERN Document Server

    Pourahmadian, Fatemeh; Haddar, Houssem

    2016-01-01

    A theoretical foundation is developed for active seismic reconstruction of fractures endowed with spatially-varying interfacial condition (e.g.~partially-closed fractures, hydraulic fractures). The proposed indicator functional carries a superior localization property with no significant sensitivity to the fracture's contact condition, measurement errors, and illumination frequency. This is accomplished through the paradigm of the $F_\\sharp$-factorization technique and the recently developed Generalized Linear Sampling Method (GLSM) applied to elastodynamics. The direct scattering problem is formulated in the frequency domain where the fracture surface is illuminated by a set of incident plane waves, while monitoring the induced scattered field in the form of (elastic) far-field patterns. The analysis of the well-posedness of the forward problem leads to an admissibility condition on the fracture's (linearized) contact parameters. This in turn contributes toward establishing the applicability of the $F_\\sharp...

  7. Model Checking for a General Linear Model with Nonignorable Missing Covariates

    Institute of Scientific and Technical Information of China (English)

    Zhi-hua SUN; Wai-Cheung IP; Heung WONG

    2012-01-01

    In this paper,we investigate the model checking problem for a general linear model with nonignorable missing covariates.We show that,without any parametric model assumption for the response probability,the least squares method yields consistent estimators for the linear model even if only the complete data are applied.This makes it feasible to propose two testing procedures for the corresponding model checking problem:a score type lack-of-fit test and a test based on the empirical process.The asymptotic properties of the test statistics are investigated.Both tests are shown to have asymptotic power 1 for local alternatives converging to the null at the rate n-(r),0 ≤ (r) < 1/2.Simulation results show that both tests perform satisfactorily.

  8. General Formulations of Finite-field Method Classified by Symmetry for Molecular Linear and Nonlinear Polarizabilities

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The formulations of the finite-field approach to calculate the linear and non-linear optical coefficients mi, aij, bijk and gijkl of a molecular system with different symmetries have been deduced and summarized. The possible choices of the energy sets of the 48 frequent point groups have been optimized and categorized into 11 classes. With the restriction of symmetry operators, a minimum of 9, no more than 21 energy points have to be calculated in order to determine the coefficients, except in the case of the first class to which C1 point group belongs and in which the 34 non-relative energy points selected in our uniform and general scheme are all needed. The symmetric operators that cause some of the tensor components to vanish have been demonstrated as well.

  9. Non-cooperative stochastic differential game theory of generalized Markov jump linear systems

    CERN Document Server

    Zhang, Cheng-ke; Zhou, Hai-ying; Bin, Ning

    2017-01-01

    This book systematically studies the stochastic non-cooperative differential game theory of generalized linear Markov jump systems and its application in the field of finance and insurance. The book is an in-depth research book of the continuous time and discrete time linear quadratic stochastic differential game, in order to establish a relatively complete framework of dynamic non-cooperative differential game theory. It uses the method of dynamic programming principle and Riccati equation, and derives it into all kinds of existence conditions and calculating method of the equilibrium strategies of dynamic non-cooperative differential game. Based on the game theory method, this book studies the corresponding robust control problem, especially the existence condition and design method of the optimal robust control strategy. The book discusses the theoretical results and its applications in the risk control, option pricing, and the optimal investment problem in the field of finance and insurance, enriching the...

  10. An Adaptive Multialphabet Arithmetic Coding Based on Generalized Virtual Sliding Window

    DEFF Research Database (Denmark)

    Belyaev, Evgeny; Forchhammer, Søren; Liu, Kai

    2017-01-01

    We propose a novel efficient multialphabet multiplication-free adaptive arithmetic coder. First, we generalize probability estimation via virtual sliding window for the multialphabet case and show that it does not require multiplications and provides a tradeoff between the probability adaptation...... speed and the precision of the probability estimation. Second, we show how the generalized virtual sliding window can be used to eliminate multiplications and divisions. Finally, we demonstrate that the proposed arithmetic coder provides better compression performance than existing implementations based...

  11. Global Convergence of Adaptive Generalized Predictive Controller Based on Least Squares Algorithm

    Institute of Scientific and Technical Information of China (English)

    张兴会; 陈增强; 袁著祉

    2003-01-01

    Some papers on stochastic adaptive control schemes have established convergence algorithm using a leastsquares parameters. With the popular application of GPC, global convergence has become a key problem in automatic control theory. However, now global convergence of GPC has not been established for algorithms in computing a least squares iteration. A generalized model of adaptive generalized predictive control is presented. The global convergebce is also given on the basis of estimating the parameters of GPC by least squares algorithm.

  12. Assessing correlation of clustered mixed outcomes from a multivariate generalized linear mixed model.

    Science.gov (United States)

    Chen, Hsiang-Chun; Wehrly, Thomas E

    2015-02-20

    The classic concordance correlation coefficient measures the agreement between two variables. In recent studies, concordance correlation coefficients have been generalized to deal with responses from a distribution from the exponential family using the univariate generalized linear mixed model. Multivariate data arise when responses on the same unit are measured repeatedly by several methods. The relationship among these responses is often of interest. In clustered mixed data, the correlation could be present between repeated measurements either within the same observer or between different methods on the same subjects. Indices for measuring such association are needed. This study proposes a series of indices, namely, intra-correlation, inter-correlation, and total correlation coefficients to measure the correlation under various circumstances in a multivariate generalized linear model, especially for joint modeling of clustered count and continuous outcomes. The proposed indices are natural extensions of the concordance correlation coefficient. We demonstrate the methodology with simulation studies. A case example of osteoarthritis study is provided to illustrate the use of these proposed indices. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Galaxy Bias and non-Linear Structure Formation in General Relativity

    CERN Document Server

    Baldauf, Tobias; Senatore, Leonardo; Zaldarriaga, Matias

    2011-01-01

    Length scales probed by large scale structure surveys are becoming closer to the horizon scale. Further, it has been recently understood that non-Gaussianity in the initial conditions could show up in a scale dependence of the bias of galaxies at the largest distances. It is therefore important to include General Relativistic effects. Here we provide a General Relativistic generalization of the bias, valid both for Gaussian and non-Gaussian initial conditions. The collapse of objects happens on very small scales, while long-wavelength modes are always in the quasi linear regime. Around every collapsing region, it is therefore possible to find a reference frame that is valid for all times and where the space time is almost flat: the Fermi frame. Here the Newtonian approximation is applicable and the equations of motion are the ones of the N-body codes. The effects of long-wavelength modes are encoded in the mapping from the cosmological frame to the local frame. For the linear bias, the effect of the long-wave...

  14. A General Method for Solving Systems of Non-Linear Equations

    Science.gov (United States)

    Nachtsheim, Philip R.; Deiss, Ron (Technical Monitor)

    1995-01-01

    The method of steepest descent is modified so that accelerated convergence is achieved near a root. It is assumed that the function of interest can be approximated near a root by a quadratic form. An eigenvector of the quadratic form is found by evaluating the function and its gradient at an arbitrary point and another suitably selected point. The terminal point of the eigenvector is chosen to lie on the line segment joining the two points. The terminal point found lies on an axis of the quadratic form. The selection of a suitable step size at this point leads directly to the root in the direction of steepest descent in a single step. Newton's root finding method not infrequently diverges if the starting point is far from the root. However, the current method in these regions merely reverts to the method of steepest descent with an adaptive step size. The current method's performance should match that of the Levenberg-Marquardt root finding method since they both share the ability to converge from a starting point far from the root and both exhibit quadratic convergence near a root. The Levenberg-Marquardt method requires storage for coefficients of linear equations. The current method which does not require the solution of linear equations requires more time for additional function and gradient evaluations. The classic trade off of time for space separates the two methods.

  15. Adaptive fault-tolerant control of linear time-invariant systems in the presence of actuator saturation

    Institute of Scientific and Technical Information of China (English)

    Wei GUAN; Guanghong YANG

    2009-01-01

    This paper studies the problem of designing adaptive fault-tolerant controllers for linear time-invariant systems with actuator saturation.New methods for designing indirect adaptive fault-tolerant controllers via state feedback are presented for actuator fault compensations.Based on the on-line estimation of eventual faults,the adaptive fault-tolerant controller parameters are updating automatically to compensate the fault effects on systems.The designs are developed in the framework of linear matrix inequality (LMI) approach,which can enlarge the domain of attraction of closed-loop systems in the cases of actuator saturation and actuator failures.Two examples are given to illustrate the effectiveness of the design method.

  16. A General Linear Wave Theory for Water Waves Propagating over Uneven Porous Bottoms

    Institute of Scientific and Technical Information of China (English)

    锁要红; 黄虎

    2004-01-01

    Starting from the widespread phenomena of porous bottoms in the near shore region, considering fully the diversity of bottom topography and wave number variation, and including the effect of evanescent modes, a general linear wave theory for water waves propagating over uneven porous bottoms in the near shore region is established by use of Green's second identity. This theory can be reduced to a number of the most typical mild-slope equations currently in use and provide a reliable research basis for follow-up development of nonlinear water wave theory involving porous bottoms.

  17. Rate of strong consistency of quasi maximum likelihood estimate in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    YUE Li; CHEN Xiru

    2004-01-01

    Under the assumption that in the generalized linear model (GLM) the expectation of the response variable has a correct specification and some other smooth conditions,it is shown that with probability one the quasi-likelihood equation for the GLM has a solution when the sample size n is sufficiently large. The rate of this solution tending to the true value is determined. In an important special case, this rate is the same as specified in the LIL for iid partial sums and thus cannot be improved anymore.

  18. ASYMPTOTIC NORMALITY OF MAXIMUM QUASI-LIKELIHOOD ESTIMATORS IN GENERALIZED LINEAR MODELS WITH FIXED DESIGN

    Institute of Scientific and Technical Information of China (English)

    Qibing GAO; Yaohua WU; Chunhua ZHU; Zhanfeng WANG

    2008-01-01

    In generalized linear models with fixed design, under the assumption ~ →∞ and otherregularity conditions, the asymptotic normality of maximum quasi-likelihood estimator (β)n, which is the root of the quasi-likelihood equation with natural link function ∑n/i=1Xi(yi-μ(X1/iβ))=0, is obtained,where λ/-n denotes the minimum eigenvalue of ∑n/i=1XiX/1/i, Xi are bounded p x q regressors, and yi are q × 1 responses.

  19. Robust root clustering for linear uncertain systems using generalized Lyapunov theory

    Science.gov (United States)

    Yedavalli, R. K.

    1993-01-01

    Consideration is given to the problem of matrix root clustering in subregions of a complex plane for linear state space models with real parameter uncertainty. The nominal matrix root clustering theory of Gutman & Jury (1981) using the generalized Liapunov equation is extended to the perturbed matrix case, and bounds are derived on the perturbation to maintain root clustering inside a given region. The theory makes it possible to obtain an explicit relationship between the parameters of the root clustering region and the uncertainty range of the parameter space.

  20. Generalized Preconditioned MHSS Method for a Class of Complex Symmetric Linear Systems

    Directory of Open Access Journals (Sweden)

    Cui-Xia Li

    2014-01-01

    Full Text Available Based on the modified Hermitian and skew-Hermitian splitting (MHSS and preconditioned MHSS (PMHSS methods, a generalized preconditioned MHSS (GPMHSS method for a class of complex symmetric linear systems is presented. Theoretical analysis gives an upper bound for the spectral radius of the iteration matrix. From a practical point of view, we have analyzed and implemented inexact GPMHSS (IGPMHSS iteration, which employs Krylov subspace methods as its inner processes. Numerical experiments are reported to confirm the efficiency of the proposed methods.

  1. An Investigation on the Parabolic Subgroups of the General Linear Groups by Using GAP

    Institute of Scientific and Technical Information of China (English)

    SaadABedaiwi; LIShang-zhi

    2004-01-01

    A typical example for the algebraic groups is the general linear groups G=GL(n,F), we have studied the structure of such groups and paid special attention to its important substructures, namely the Parabolic subgroups. For a given G we computed all the Parabolic subgroups and determined their number, depending on the fact that any finite group has a composition series and the composition factors of a composition series are simple groups which are completely classified, we report here some investigations on the computed Parabolic subgroups. This has been done with the utility of GAP.

  2. Robust root clustering for linear uncertain systems using generalized Lyapunov theory

    Science.gov (United States)

    Yedavalli, R. K.

    1993-01-01

    Consideration is given to the problem of matrix root clustering in subregions of a complex plane for linear state space models with real parameter uncertainty. The nominal matrix root clustering theory of Gutman & Jury (1981) using the generalized Liapunov equation is extended to the perturbed matrix case, and bounds are derived on the perturbation to maintain root clustering inside a given region. The theory makes it possible to obtain an explicit relationship between the parameters of the root clustering region and the uncertainty range of the parameter space.

  3. Adaptation of the phase of the human linear vestibulo-ocular reflex (LVOR) and effects on the oculomotor neural integrator

    Science.gov (United States)

    Hegemann, S.; Shelhamer, M.; Kramer, P. D.; Zee, D. S.

    2000-01-01

    The phase of the translational linear VOR (LVOR) can be adaptively modified by exposure to a visual-vestibular mismatch. We extend here our earlier work on LVOR phase adaptation, and discuss the role of the oculomotor neural integrator. Ten subjects were oscillated laterally at 0.5 Hz, 0.3 g peak acceleration, while sitting upright on a linear sled. LVOR was assessed before and after adaptation with subjects tracking the remembered location of a target at 1 m in the dark. Phase and gain were measured by fitting sine waves to the desaccaded eye movements, and comparing sled and eye position. To adapt LVOR phase, the subject viewed a computer-generated stereoscopic visual display, at a virtual distance of 1 m, that moved so as to require either a phase lead or a phase lag of 53 deg. Adaptation lasted 20 min, during which subjects were oscillated at 0.5 Hz/0.3 g. Four of five subjects produced an adaptive change in the lag condition (range 4-45 deg), and each of five produced a change in the lead condition (range 19-56 deg), as requested. Changes in drift on eccentric gaze suggest that the oculomotor velocity-to-position integrator may be involved in the phase changes.

  4. Vowel generalization and its relation to adaptation during perturbations of auditory feedback.

    Science.gov (United States)

    Reilly, Kevin J; Pettibone, Chelsea

    2017-08-23

    Repeated perturbations of auditory feedback during vowel production elicit changes not only in the production of the perturbed vowel (adaptation) but also in the production of nearby vowels that were not perturbed (generalization). The finding that adaptation generalizes to other, non-perturbed vowels suggest that sensorimotor representations for vowels are not independent; instead the goals for producing any one vowel may depend in part on the goals for other vowels. The present study investigated the dependence or independence of vowel representations by evaluating adaptation and generalization in two groups of speakers exposed to auditory perturbations of their first formant (F1) during different vowels. The speakers in both groups who adapted to the perturbation exhibited generalization in two non-perturbed vowels that were produced under masking noise. Correlation testing was performed to evaluate the relations between adaptation and generalization as well as between the generalization in the two non-perturbed vowels. These tests identified significant coupling between the F1 changes of adjacent vowels but not non-adjacent vowels. The pattern of correlation findings indicates that generalization was due in part to feedforward representations that are partly shared across adjacent vowels, possibly to maintain their acoustic contrast. Copyright © 2016, Journal of Neurophysiology.

  5. Vector generalized linear and additive models with an implementation in R

    CERN Document Server

    Yee, Thomas W

    2015-01-01

    This book presents a statistical framework that expands generalized linear models (GLMs) for regression modelling. The framework shared in this book allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. This is possible through the approximately half-a-dozen major classes of statistical models included in the book and the software infrastructure component, which makes the models easily operable.    The book’s methodology and accompanying software (the extensive VGAM R package) are directed at these limitations, and this is the first time the methodology and software are covered comprehensively in one volume. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. The demands of practical data analysis, however, require a flexibility that GLMs do not have. Data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. This book ...

  6. Rigorous asymptotic and moment-preserving diffusion approximations for generalized linear Boltzmann transport in d dimensions

    CERN Document Server

    d'Eon, Eugene

    2013-01-01

    We derive new diffusion solutions to the monoenergetic generalized linear Boltzmann transport equation (GLBE) for the stationary collision density and scalar flux about an isotropic point source in an infinite $d$-dimensional absorbing medium with isotropic scattering. We consider both classical transport theory with exponentially-distributed free paths in arbitrary dimensions as well as a number of non-classical transport theories (non-exponential random flights) that describe a broader class of transport processes within partially-correlated random media. New rigorous asymptotic diffusion approximations are derived where possible. We also generalize Grosjean's moment-preserving approach of separating the first (or uncollided) distribution from the collided portion and approximating only the latter using diffusion. We find that for any spatial dimension and for many free-path distributions Grosjean's approach produces compact, analytic approximations that are, overall, more accurate for high absorption and f...

  7. A general derivation of the subharmonic threshold for non-linear bubble oscillations.

    Science.gov (United States)

    Prosperetti, Andrea

    2013-06-01

    The paper describes an approximate but rather general derivation of the acoustic threshold for a subharmonic component to be possible in the sound scattered by an insonified gas bubble. The general result is illustrated with several specific models for the mechanical behavior of the surface coating of bubbles used as acoustic contrast agents. The approximate results are found to be in satisfactory agreement with fully non-linear numerical results in the literature. The amplitude of the first harmonic is also found by the same method. A fundamental feature identified by the analysis is that the subharmonic threshold can be considerably lowered with respect to that of an uncoated free bubble if the mechanical response of the coating varies rapidly in the neighborhood of certain specific values of the bubble radius, e.g., because of buckling.

  8. Wave packet dynamics in one-dimensional linear and nonlinear generalized Fibonacci lattices.

    Science.gov (United States)

    Zhang, Zhenjun; Tong, Peiqing; Gong, Jiangbin; Li, Baowen

    2011-05-01

    The spreading of an initially localized wave packet in one-dimensional linear and nonlinear generalized Fibonacci (GF) lattices is studied numerically. The GF lattices can be classified into two classes depending on whether or not the lattice possesses the Pisot-Vijayaraghavan property. For linear GF lattices of the first class, both the second moment and the participation number grow with time. For linear GF lattices of the second class, in the regime of a weak on-site potential, wave packet spreading is close to ballistic diffusion, whereas in the regime of a strong on-site potential, it displays stairlike growth in both the second moment and the participation number. Nonlinear GF lattices are then investigated in parallel. For the first class of nonlinear GF lattices, the second moment of the wave packet still grows with time, but the corresponding participation number does not grow simultaneously. For the second class of nonlinear GF lattices, an analogous phenomenon is observed for the weak on-site potential only. For a strong on-site potential that leads to an enhanced nonlinear self-trapping effect, neither the second moment nor the participation number grows with time. The results can be useful in guiding experiments on the expansion of noninteracting or interacting cold atoms in quasiperiodic optical lattices.

  9. Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

    Directory of Open Access Journals (Sweden)

    A. Cabezas

    2010-02-01

    Full Text Available Sediment, Total Organic Carbon (TOC and total nitrogen (TN accumulation during one overbank flood (1.15 y were examined at one reach of the Middle Ebro River (NE Spain for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m2 study plots consisting on three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of another factors as vegetation morphology should be included in further studies to explain small scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment solid concentration.

  10. Spatial variability in floodplain sedimentation: the use of generalized linear mixed-effects models

    Science.gov (United States)

    Cabezas, A.; Angulo-Martínez, M.; Gonzalez-Sanchís, M.; Jimenez, J. J.; Comín, F. A.

    2010-08-01

    Sediment, Total Organic Carbon (TOC) and total nitrogen (TN) accumulation during one overbank flood (1.15 y return interval) were examined at one reach of the Middle Ebro River (NE Spain) for elucidating spatial patterns. To achieve this goal, four areas with different geomorphological features and located within the study reach were examined by using artificial grass mats. Within each area, 1 m2 study plots consisting of three pseudo-replicates were placed in a semi-regular grid oriented perpendicular to the main channel. TOC, TN and Particle-Size composition of deposited sediments were examined and accumulation rates estimated. Generalized linear mixed-effects models were used to analyze sedimentation patterns in order to handle clustered sampling units, specific-site effects and spatial self-correlation between observations. Our results confirm the importance of channel-floodplain morphology and site micro-topography in explaining sediment, TOC and TN deposition patterns, although the importance of other factors as vegetation pattern should be included in further studies to explain small-scale variability. Generalized linear mixed-effect models provide a good framework to deal with the high spatial heterogeneity of this phenomenon at different spatial scales, and should be further investigated in order to explore its validity when examining the importance of factors such as flood magnitude or suspended sediment concentration.

  11. Blended General Linear Methods based on Boundary Value Methods in the GBDF family

    CERN Document Server

    Brugnano, Luigi

    2010-01-01

    Among the methods for solving ODE-IVPs, the class of General Linear Methods (GLMs) is able to encompass most of them, ranging from Linear Multistep Formulae (LMF) to RK formulae. Moreover, it is possible to obtain methods able to overcome typical drawbacks of the previous classes of methods. For example, order barriers for stable LMF and the problem of order reduction for RK methods. Nevertheless, these goals are usually achieved at the price of a higher computational cost. Consequently, many efforts have been made in order to derive GLMs with particular features, to be exploited for their efficient implementation. In recent years, the derivation of GLMs from particular Boundary Value Methods (BVMs), namely the family of Generalized BDF (GBDF), has been proposed for the numerical solution of stiff ODE-IVPs. In particular, this approach has been recently developed, resulting in a new family of L-stable GLMs of arbitrarily high order, whose theory is here completed and fully worked-out. Moreover, for each one o...

  12. Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests.

    Science.gov (United States)

    Liu, Wei; Pantoja-Galicia, Norberto; Zhang, Bo; Kotz, Richard M; Pennello, Gene; Zhang, Hui; Jacob, Jessie; Zhang, Zhiwei

    2017-06-01

    Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski-Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader-test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.

  13. Thermodynamic bounds and general properties of optimal efficiency and power in linear responses.

    Science.gov (United States)

    Jiang, Jian-Hua

    2014-10-01

    We study the optimal exergy efficiency and power for thermodynamic systems with an Onsager-type "current-force" relationship describing the linear response to external influences. We derive, in analytic forms, the maximum efficiency and optimal efficiency for maximum power for a thermodynamic machine described by a N×N symmetric Onsager matrix with arbitrary integer N. The figure of merit is expressed in terms of the largest eigenvalue of the "coupling matrix" which is solely determined by the Onsager matrix. Some simple but general relationships between the power and efficiency at the conditions for (i) maximum efficiency and (ii) optimal efficiency for maximum power are obtained. We show how the second law of thermodynamics bounds the optimal efficiency and the Onsager matrix and relate those bounds together. The maximum power theorem (Jacobi's Law) is generalized to all thermodynamic machines with a symmetric Onsager matrix in the linear-response regime. We also discuss systems with an asymmetric Onsager matrix (such as systems under magnetic field) for a particular situation and we show that the reversible limit of efficiency can be reached at finite output power. Cooperative effects are found to improve the figure of merit significantly in systems with multiply cross-correlated responses. Application to example systems demonstrates that the theory is helpful in guiding the search for high performance materials and structures in energy researches.

  14. Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

    Science.gov (United States)

    Wang, Gang; Wang, Chaoli; Du, Qinghui; Cai, Xuan

    2016-10-01

    In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.

  15. On the Generalization of the Timoshenko Beam Model Based on the Micropolar Linear Theory: Static Case

    Directory of Open Access Journals (Sweden)

    Andrea Nobili

    2015-01-01

    Full Text Available Three generalizations of the Timoshenko beam model according to the linear theory of micropolar elasticity or its special cases, that is, the couple stress theory or the modified couple stress theory, recently developed in the literature, are investigated and compared. The analysis is carried out in a variational setting, making use of Hamilton’s principle. It is shown that both the Timoshenko and the (possibly modified couple stress models are based on a microstructural kinematics which is governed by kinosthenic (ignorable terms in the Lagrangian. Despite their difference, all models bring in a beam-plane theory only one microstructural material parameter. Besides, the micropolar model formally reduces to the couple stress model upon introducing the proper constraint on the microstructure kinematics, although the material parameter is generally different. Line loading on the microstructure results in a nonconservative force potential. Finally, the Hamiltonian form of the micropolar beam model is derived and the canonical equations are presented along with their general solution. The latter exhibits a general oscillatory pattern for the microstructure rotation and stress, whose behavior matches the numerical findings.

  16. A generalization of the MDS method by mixed integer linear and nonlinear mathematical models

    Directory of Open Access Journals (Sweden)

    Sadegh Niroomand

    2014-09-01

    Full Text Available The Multi-Dimensional Scaling (MDS method is used in statistics to detect hidden interrelations among multi-dimensional data and it has a wide range of applications. The method’s input is a matrix that describes the similarity/dissimilarity among objects of unknown dimension. The objects are generally reconstructed as points of a lower dimensional space to reveal the geometric configuration of the objects. The original MDS method uses Euclidean distance, for measuring both the distance of the reconstructed points and the bias of the reconstructed distances from the original similarity values. In this paper, these distances are distinguished, and distances other than Euclidean are also used, generalizing the MDS method. Two different distances may be used for the two different purposes. Therefore the instances of the generalized MDS model are denoted as  model, where the first distance is the type of distance of the reconstructed points and the second one measures the bias of the reconstructed distances and the similarity values. In the case of   and   distances mixed-integer programming models are provided. The computational experiences show that the generalized model can catch the key properties of the original configuration, if any exist. Keywords: Multivariate Analysis; Multi-Dimensional Scaling; Optimization; Mixed Integer Linear Programming; Statistics.

  17. A study of passive and adaptive hydraulic engine mount systems with emphasis on non-linear characteristics

    Science.gov (United States)

    Kim, G.; Singh, R.

    1995-01-01

    Passive hydraulic mounts exhibit excitation frequency variant and deflection amplitude sensitive stiffness and damping properties. Such non-linear dynamic characteristics are examined by using analytical and experimental methods, both at the device level and within the context of a simplified vehicle model. A new lumped parameter non-linear mathematical model of the hydraulic mount is developed by simulating its decoupler switching mechanism and inertia track dynamics. The low frequency performance features and limitations of several passive mounts are made clear through the non-linear vehicle model simulation and comparable laboratory vibration tests. The high frequency performance problems of the passive hydraulic mount are identified by applying the quasi-linear analysis method. Based on these results, a new adaptive mount system is developed which exhibits broad bandwidth performance features up to 250 Hz. It implements an on-off damping control mode by using engine intake manifold vacuum and a microprocessor based solenoid valve controller. A laboratory bench set-up has already demonstrated its operational feasibility. Through analytical methods, it is observed that our adaptive mount provides superior dynamic performance to passive engine mounts and comparable performance to a small scale active mount over a wide frequency range, given the engine mounting resonance control, shock absorption and vibration isolation performance requirements. Although technical prospects of the proposed adaptive system appear promising, the in situperformance needs to be evaluated.

  18. Taylor series approximation of semi-blind best linear unbiased channel estimates for the general linear model

    OpenAIRE

    Pladdy, Christopher; Nerayanuru, Sreenivasa M.; Fimoff, Mark; Özen, Serdar; Zoltowski, Michael

    2004-01-01

    We present a low complexity approximate method for semi-blind best linear unbiased estimation (BLUE) of a channel impulse response vector (CIR) for a communication system, which utilizes a periodically transmitted training sequence, within a continuous stream of information symbols. The algorithm achieves slightly degraded results at a much lower complexity than directly computing the BLUE CIR estimate. In addition, the inverse matrix required to invert the weighted normal equations to solve ...

  19. Generalization patterns for reach adaptation and proprioceptive recalibration differ after visuomotor learning.

    Science.gov (United States)

    Cressman, Erin K; Henriques, Denise Y P

    2015-07-01

    Visuomotor learning results in changes in both motor and sensory systems (Cressman EK, Henriques DY. J Neurophysiol 102: 3505-3518, 2009), such that reaches are adapted and sense of felt hand position recalibrated after reaching with altered visual feedback of the hand. Moreover, visuomotor learning has been shown to generalize such that reach adaptation achieved at a trained target location can influence reaches to novel target directions (Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. J Neurosci 20: 8916-8924, 2000). We looked to determine whether proprioceptive recalibration also generalizes to novel locations. Moreover, we looked to establish the relationship between reach adaptation and changes in sense of felt hand position by determining whether proprioceptive recalibration generalizes to novel targets in a similar manner as reach adaptation. On training trials, subjects reached to a single target with aligned or misaligned cursor-hand feedback, in which the cursor was either rotated or scaled in extent relative to hand movement. After reach training, subjects reached to the training target and novel targets (including targets from a second start position) without visual feedback to assess generalization of reach adaptation. Subjects then performed a proprioceptive estimation task, in which they indicated the position of their hand relative to visual reference markers placed at similar locations as the trained and novel reach targets. Results indicated that shifts in hand position generalized across novel locations, independent of reach adaptation. Thus these distinct sensory and motor generalization patterns suggest that reach adaptation and proprioceptive recalibration arise from independent error signals and that changes in one system cannot guide adjustments in the other.

  20. Adaptive generalized projective synchronization of two different chaotic systems with unknown parameters

    Institute of Scientific and Technical Information of China (English)

    Zhang Ruo-Xun; Yang Shi-Ping

    2008-01-01

    This paper presents a general method of the generalized projective synchronization and the parameter identification between two different chaotic systems with unknown parameters.This approach is based on Lyapunov stability theory,and employs a combination of feedback control and adaptive control.With this method one can achieve the generalized projective synchronization and realize the parameter identifications between almost all chaotic (hyperchaotic) systems with unknown parameters.Numerical simulations results are presented to demonstrate the effectiveness of the method.

  1. The quantum general linear supergroup,canonical bases and Kazhdan-Lusztig polynomials

    Institute of Scientific and Technical Information of China (English)

    ZHANG HeChun

    2009-01-01

    Canonical bases of the tensor powers of the natural Uq(glm|n)-module V are constructed by adapting the work of Frenkel,Khovanov and Kirrilov to the quantum supergroup setting.This result is generalized in several directions.We first construct the canonical bases of the Z2-graded symmetric algebra of V and tensor powers of this superalgebra;then construct canonical bases for the superalgebra Oq(Mm|n) of a quantum (m,n) x (m,n)-supermatrix;and finally deduce from the latter result the canonical basis of every irreducible tensor module for Uq(glm|n) by applying a quantum analogue of the Borel-Weil construction.

  2. Prediction of Rotor Spun Yarn Strength Using Adaptive Neuro-fuzzy Inference System and Linear Multiple Regression Methods

    Institute of Scientific and Technical Information of China (English)

    NURWAHA Deogratias; WANG Xin-hou

    2008-01-01

    This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.

  3. The heritability of general cognitive ability increases linearly from childhood to young adulthood.

    Science.gov (United States)

    Haworth, C M A; Wright, M J; Luciano, M; Martin, N G; de Geus, E J C; van Beijsterveldt, C E M; Bartels, M; Posthuma, D; Boomsma, D I; Davis, O S P; Kovas, Y; Corley, R P; Defries, J C; Hewitt, J K; Olson, R K; Rhea, S-A; Wadsworth, S J; Iacono, W G; McGue, M; Thompson, L A; Hart, S A; Petrill, S A; Lubinski, D; Plomin, R

    2010-11-01

    Although common sense suggests that environmental influences increasingly account for individual differences in behavior as experiences accumulate during the course of life, this hypothesis has not previously been tested, in part because of the large sample sizes needed for an adequately powered analysis. Here we show for general cognitive ability that, to the contrary, genetic influence increases with age. The heritability of general cognitive ability increases significantly and linearly from 41% in childhood (9 years) to 55% in adolescence (12 years) and to 66% in young adulthood (17 years) in a sample of 11 000 pairs of twins from four countries, a larger sample than all previous studies combined. In addition to its far-reaching implications for neuroscience and molecular genetics, this finding suggests new ways of thinking about the interface between nature and nurture during the school years. Why, despite life's 'slings and arrows of outrageous fortune', do genetically driven differences increasingly account for differences in general cognitive ability? We suggest that the answer lies with genotype-environment correlation: as children grow up, they increasingly select, modify and even create their own experiences in part based on their genetic propensities.

  4. Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

    KAUST Repository

    Cheng, Guang

    2014-02-01

    We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2014 ISI/BS.

  5. Adaptive lag synchronization based topology identification scheme of uncertain general complex dynamical networks

    Science.gov (United States)

    Che, Y.; Li, R. X.; Han, C. X.; Wang, J.; Cui, S. G.; Deng, B.; Wei, X.

    2012-08-01

    This paper presents an adaptive lag synchronization based method for simultaneous identification of topology and parameters of uncertain general complex dynamical networks with and without time delays. Based on Lyapunov stability theorem and LaSalle's invariance principle, an adaptive controller is designed to realize lag synchronization between drive and response systems, meanwhile, identification criteria of network topology and system parameters are obtained. Numerical simulations illustrate the effectiveness of the proposed method.

  6. Geometric and growth rate tests of General Relativity with recovered linear cosmological perturbations

    CERN Document Server

    Wilson, Michael J

    2016-01-01

    I investigate the consistency of the VIMOS Public Extragalactic Redshift Survey v7 galaxy sample with the expansion history and linear growth rate predicted by General Relativity (GR) and a Planck (2015) cosmology. To do so, I measure the redshift-space power spectrum, which is anisotropic due to both redshift-space distortions (RSD) and the Alcock-Paczynski (AP) effect. In Chapter 6, I place constraints of $f \\sigma_8(0.76) = 0.44 \\pm 0.04$ and $f \\sigma_8(1.05) = 0.28 \\pm 0.08$, which remain consistent with GR at 95% confidence. Marginalising over the anisotropic AP effect degrades the constraints by a factor of three but allows $F_{AP} \\equiv (1+z) D_A H/c$ to be simultaneously constrained. The VIPERS v7 joint-posterior on $(f \\sigma_8, F_{AP})$ shows no compelling deviation from GR. Chapter 7 investigates the inclusion of a simple density transform: `clipping' prior to the RSD analysis. This tackles the root-cause of non-linearity and may extend the validity of perturbation theory. Moreover, this marked s...

  7. Developing minds of tomorrow: exploring students' strategies involved in the generalization of linear patterns

    Directory of Open Access Journals (Sweden)

    Areej IsamBarham

    2011-11-01

    Full Text Available The study investigates students' strategies involved in the generalization of "linear patterns". The study followed thequalitative research approach by conducting task-based interviews with twenty-nine primary second grade students fromdifferent high, intermediate and low ability levels. Results of the study presented several strategies involved in thegeneralization of the patterns including visual, auditory, mental, finger counting, verbal counting, and traditional (paper andpencil strategies. The findings revealed that the type of the assigned pattern (simple or complex and the type of the structureof the pattern itself (increasing or decreasing play a big role for students' strategies involved to either discover the rule of thepattern or to extend it. However, students in early ages could master several skills and choose appropriate procedures to dealwith patterns, which indicate that they could develop their algebraic thinking from early stages. Findings of the study alsorevealed that using different senses, using the idea of coins, using the numbers line, recognizing musical sounds, using concretematerials like fingers, applying different visual and mental strategies, and even applying traditional calculations could helpstudents to work with “linear patterns". It is recommended that teachers introduce different strategies and procedures inteaching patterns to meet the needs of students as different learners, give them the opportunities to develop their thinkingstrategies and explore their thoughts. More research is recommended to explore students' strategies involved in thegeneralization of different kinds of patters at different stages.

  8. On some problems of weak consistency of quasi-maximum likelihood estimates in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In this paper,we explore some weakly consistent properties of quasi-maximum likelihood estimates(QMLE) concerning the quasi-likelihood equation in=1 Xi(yi-μ(Xiβ)) = 0 for univariate generalized linear model E(y |X) = μ(X’β).Given uncorrelated residuals {ei = Yi-μ(Xiβ0),1 i n} and other conditions,we prove that βn-β0 = Op(λn-1/2) holds,where βn is a root of the above equation,β0 is the true value of parameter β and λn denotes the smallest eigenvalue of the matrix Sn = ni=1 XiXi.We also show that the convergence rate above is sharp,provided independent non-asymptotically degenerate residual sequence and other conditions.Moreover,paralleling to the elegant result of Drygas(1976) for classical linear regression models,we point out that the necessary condition guaranteeing the weak consistency of QMLE is Sn-1→ 0,as the sample size n →∞.

  9. On some problems of weak consistency of quasi-maximum likelihood estimates in generalized linear models

    Institute of Scientific and Technical Information of China (English)

    ZHANG SanGuo; LIAO Yuan

    2008-01-01

    In this paper, we explore some weakly consistent properties of quasi-maximum likelihood estimates(QMLE)concerning the quasi-likelihood equation ∑ni=1 Xi(yi-μ(X1iβ)) =0 for univariate generalized linear model E(y|X) =μ(X1β). Given uncorrelated residuals{ei=Yi-μ(X1iβ0), 1≤i≤n}and other conditions, we prove that (β)n-β0=Op(λ--1/2n)holds, where (β)n is a root of the above equation,β0 is the true value of parameter β and λ-n denotes the smallest eigenvalue of the matrix Sn=Σni=1 XiX1i. We also show that the convergence rate above is sharp, provided independent nonasymptotically degenerate residual sequence and other conditions. Moreover, paralleling to the elegant result of Drygas(1976)for classical linear regression models,we point out that the necessary condition guaranteeing the weak consistency of QMLE is S-1n→0, as the sample size n→∞.

  10. MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package

    Directory of Open Access Journals (Sweden)

    Jarrod Had

    2010-02-01

    Full Text Available Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(binominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression, and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.

  11. The Potential in General Linear Electrodynamics: Causal Structure, Propagators and Quantization

    CERN Document Server

    Pfeifer, Christian

    2016-01-01

    An axiomatic approach to electrodynamics reveals that Maxwell electrodynamics is just one instance of a variety of theories for which the name electrodynamics is justified. They all have in common that their fundamental input are Maxwell's equations $\\textrm{d} F = 0$ (or $F = \\textrm{d} A$) and $\\textrm{d} H = J$ and a constitutive law $H = \\# F$ which relates the field strength two-form $F$ and the excitation two-form $H$. A local and linear constitutive law defines what is called general linear electrodynamics whose best known application are the effective description of electrodynamics inside media including, e.g., birefringence. We will analyze the classical theory of the electromagnetic potential $A$ before we use methods familiar from mathematical quantum field theory in curved spacetimes to quantize it in a locally covariant way. Our analysis of the classical theory contains the derivation of retarded and advanced propagators, the analysis of the causal structure on the basis of the constitutive law (...

  12. General Explicit Solution of Planar Weakly Delayed Linear Discrete Systems and Pasting Its Solutions

    Directory of Open Access Journals (Sweden)

    Josef Diblík

    2014-01-01

    Full Text Available Planar linear discrete systems with constant coefficients and delays x(k+1=Ax(k+∑l=1n‍Blxl(k-ml are considered where k∈ℤ0∞:={0,1,…,∞}, m1,m2,…,mn are constant integer delays, 0linear differential systems with constant coefficients and special delays when the initially infinite dimensional space of solutions on the initial interval turns (after several steps into a finite dimensional set of solutions. For every possible case, explicit general solutions are constructed and, finally, results on the dimensionality of the space of solutions are obtained.

  13. Tuning, Diagnostics & Data Preparation for Generalized Linear Models Supervised Algorithm in Data Mining Technologies

    Directory of Open Access Journals (Sweden)

    Sachin Bhaskar

    2015-07-01

    Full Text Available Data mining techniques are the result of a long process of research and product development. Large amount of data are searched by the practice of Data Mining to find out the trends and patterns that go beyond simple analysis. For segmentation of data and also to evaluate the possibility of future events, complex mathematical algorithms are used here. Specific algorithm produces each Data Mining model. More than one algorithms are used to solve in best way by some Data Mining problems. Data Mining technologies can be used through Oracle. Generalized Linear Models (GLM Algorithm is used in Regression and Classification Oracle Data Mining functions. For linear modelling, GLM is one the popular statistical techniques. For regression and binary classification, GLM is implemented by Oracle Data Mining. Row diagnostics as well as model statistics and extensive co-efficient statistics are provided by GLM. It also supports confidence bounds.. This paper outlines and produces analysis of GLM algorithm, which will guide to understand the tuning, diagnostics & data preparation process and the importance of Regression & Classification supervised Oracle Data Mining functions and it is utilized in marketing, time series prediction, financial forecasting, overall business planning, trend analysis, environmental modelling, biomedical and drug response modelling, etc.

  14. A generalized fuzzy linear programming approach for environmental management problem under uncertainty.

    Science.gov (United States)

    Fan, Yurui; Huang, Guohe; Veawab, Amornvadee

    2012-01-01

    In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.

  15. Towards downscaling precipitation for Senegal - An approach based on generalized linear models and weather types

    Science.gov (United States)

    Rust, H. W.; Vrac, M.; Lengaigne, M.; Sultan, B.

    2012-04-01

    Changes in precipitation patterns with potentially less precipitation and an increasing risk for droughts pose a threat to water resources and agricultural yields in Senegal. Precipitation in this region is dominated by the West-African Monsoon being active from May to October, a seasonal pattern with inter-annual to decadal variability in the 20th century which is likely to be affected by climate change. We built a generalized linear model for a full spatial description of rainfall in Senegal. The model uses season, location, and a discrete set of weather types as predictors and yields a spatially continuous description of precipitation occurrences and intensities. Weather types have been defined on NCEP/NCAR reanalysis using zonal and meridional winds, as well as relative humidity. This model is suitable for downscaling precipitation, particularly precipitation occurrences relevant for drough risk mapping.

  16. Dynamic Average Consensus and Consensusability of General Linear Multiagent Systems with Random Packet Dropout

    Directory of Open Access Journals (Sweden)

    Wen-Min Zhou

    2013-01-01

    Full Text Available This paper is concerned with the consensus problem of general linear discrete-time multiagent systems (MASs with random packet dropout that happens during information exchange between agents. The packet dropout phenomenon is characterized as being a Bernoulli random process. A distributed consensus protocol with weighted graph is proposed to address the packet dropout phenomenon. Through introducing a new disagreement vector, a new framework is established to solve the consensus problem. Based on the control theory, the perturbation argument, and the matrix theory, the necessary and sufficient condition for MASs to reach mean-square consensus is derived in terms of stability of an array of low-dimensional matrices. Moreover, mean-square consensusable conditions with regard to network topology and agent dynamic structure are also provided. Finally, the effectiveness of the theoretical results is demonstrated through an illustrative example.

  17. Quasi-Maximum Likelihood Estimators in Generalized Linear Models with Autoregressive Processes

    Institute of Scientific and Technical Information of China (English)

    Hong Chang HU; Lei SONG

    2014-01-01

    The paper studies a generalized linear model (GLM) yt=h(xTtβ)+εt, t=1, 2, . . . , n, whereε1=η1,εt=ρεt-1+ηt, t=2,3,...,n, h is a continuous diff erentiable function,ηt’s are independent and identically distributed random errors with zero mean and finite varianceσ 2. Firstly, the quasi-maximum likelihood (QML) estimators ofβ,ρandσ 2 are given. Secondly, under mild conditions, the asymptotic properties (including the existence, weak consistency and asymptotic distribution) of the QML estimators are investigated. Lastly, the validity of method is illuminated by a simulation example.

  18. A Fuzzy Approach Using Generalized Dinkelbach’s Algorithm for Multiobjective Linear Fractional Transportation Problem

    Directory of Open Access Journals (Sweden)

    Nurdan Cetin

    2014-01-01

    Full Text Available We consider a multiobjective linear fractional transportation problem (MLFTP with several fractional criteria, such as, the maximization of the transport profitability like profit/cost or profit/time, and its two properties are source and destination. Our aim is to introduce MLFTP which has not been studied in literature before and to provide a fuzzy approach which obtain a compromise Pareto-optimal solution for this problem. To do this, first, we present a theorem which shows that MLFTP is always solvable. And then, reducing MLFTP to the Zimmermann’s “min” operator model which is the max-min problem, we construct Generalized Dinkelbach’s Algorithm for solving the obtained problem. Furthermore, we provide an illustrative numerical example to explain this fuzzy approach.

  19. Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models

    CERN Document Server

    Hughes, John

    2010-01-01

    Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model (SGLMM) offers a very popular and flexible approach to modeling such data, but the SGLMM suffers from three major shortcomings: (1) uninterpretability of parameters due to spatial confounding, (2) variance inflation due to spatial confounding, and (3) high-dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the SGLMM that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count, and Gaussian spatial datasets, and to a large infant mortali...

  20. Bayesian model choice and information criteria in sparse generalized linear models

    CERN Document Server

    Foygel, Rina

    2011-01-01

    We consider Bayesian model selection in generalized linear models that are high-dimensional, with the number of covariates p being large relative to the sample size n, but sparse in that the number of active covariates is small compared to p. Treating the covariates as random and adopting an asymptotic scenario in which p increases with n, we show that Bayesian model selection using certain priors on the set of models is asymptotically equivalent to selecting a model using an extended Bayesian information criterion. Moreover, we prove that the smallest true model is selected by either of these methods with probability tending to one. Having addressed random covariates, we are also able to give a consistency result for pseudo-likelihood approaches to high-dimensional sparse graphical modeling. Experiments on real data demonstrate good performance of the extended Bayesian information criterion for regression and for graphical models.

  1. A New General Linear Convolution Model for fMRI Data Process

    Institute of Scientific and Technical Information of China (English)

    YUAN Hong; CHEN Hua-fu; YAO De-zhong

    2005-01-01

    General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis. However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation, according to the principle of GLM, a new convolution model is presented by a new dynamic function convolving with design-matrix, which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among vland v2 areas of visual cortex, and also verified the validity of this technique.

  2. Generalization of the ordinary state-based peridynamic model for isotropic linear viscoelasticity

    Science.gov (United States)

    Delorme, Rolland; Tabiai, Ilyass; Laberge Lebel, Louis; Lévesque, Martin

    2017-02-01

    This paper presents a generalization of the original ordinary state-based peridynamic model for isotropic linear viscoelasticity. The viscoelastic material response is represented using the thermodynamically acceptable Prony series approach. It can feature as many Prony terms as required and accounts for viscoelastic spherical and deviatoric components. The model was derived from an equivalence between peridynamic viscoelastic parameters and those appearing in classical continuum mechanics, by equating the free energy densities expressed in both frameworks. The model was simplified to a uni-dimensional expression and implemented to simulate a creep-recovery test. This implementation was finally validated by comparing peridynamic predictions to those predicted from classical continuum mechanics. An exact correspondence between peridynamics and the classical continuum approach was shown when the peridynamic horizon becomes small, meaning peridynamics tends toward classical continuum mechanics. This work provides a clear and direct means to researchers dealing with viscoelastic phenomena to tackle their problem within the peridynamic framework.

  3. Master equation solutions in the linear regime of characteristic formulation of general relativity

    CERN Document Server

    M., C E Cedeño

    2015-01-01

    From the field equations in the linear regime of the characteristic formulation of general relativity, Bishop, for a Schwarzschild's background, and M\\"adler, for a Minkowski's background, were able to show that it is possible to derive a fourth order ordinary differential equation, called master equation, for the $J$ metric variable of the Bondi-Sachs metric. Once $\\beta$, another Bondi-Sachs potential, is obtained from the field equations, and $J$ is obtained from the master equation, the other metric variables are solved integrating directly the rest of the field equations. In the past, the master equation was solved for the first multipolar terms, for both the Minkowski's and Schwarzschild's backgrounds. Also, M\\"adler recently reported a generalisation of the exact solutions to the linearised field equations when a Minkowski's background is considered, expressing the master equation family of solutions for the vacuum in terms of Bessel's functions of the first and the second kind. Here, we report new sol...

  4. Ultra Linear Low-loss Varactors & Circuits for Adaptive RF Systems

    NARCIS (Netherlands)

    Huang, C.

    2010-01-01

    With the evolution of wireless communication, varactors can play an important role in enabling adaptive transceivers as well as phase-diversity systems. This thesis presents various varactor diode-based circuit topologies that facilitate RF adaptivity. The proposed varactor configurations can act as

  5. Sensor and Actuator Fault Detection and Isolation in Nonlinear System using Multi Model Adaptive Linear Kalman Filter

    Directory of Open Access Journals (Sweden)

    M. Manimozhi

    2014-05-01

    Full Text Available Fault Detection and Isolation (FDI using Linear Kalman Filter (LKF is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF for Fault Detection and Isolation (FDI of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously.

  6. A novel frequency tracking method based on complex adaptive linear neural network state vector in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Sadinezhad, I.; Joorabian, M. [Electrical Engineering Department, Shahid Chamran University, Ahvaz 61355 (Iran)

    2009-08-15

    This paper presents the application of a Complex Adaptive Linear Neural Network (CADALINE) in tracking the fundamental power system frequency. In this method, by using stationary-axes Park transformation in addition to producing a complex input measurement, the decaying DC offset is eliminated. As the proposed method uses first-order differentiator to estimate frequency changes, a Hamming filter is used to smoothen the response and cancel high-frequency noises. The most distinguishing features of the proposed method are the reduction in the size of observation state vector required by a simple Adaptive Linear Neural Network (ADALINE) and increase in the accuracy and convergence speed under transient conditions. This paper concludes with the presentation of the representative results obtained in numerical simulation and simulation in PSCAD/EMTDC software as well as in practical study. (author)

  7. Dynamic analysis on generalized linear elastic body subjected to large scale rigid rotations

    Institute of Scientific and Technical Information of China (English)

    刘占芳; 颜世军; 符志

    2013-01-01

    The dynamic analysis of a generalized linear elastic body undergoing large rigid rotations is investigated. The generalized linear elastic body is described in kine-matics through translational and rotational deformations, and a modified constitutive relation for the rotational deformation is proposed between the couple stress and the curvature tensor. Thus, the balance equations of momentum and moment are used for the motion equations of the body. The floating frame of reference formulation is applied to the elastic body that conducts rotations about a fixed axis. The motion-deformation coupled model is developed in which three types of inertia forces along with their incre-ments are elucidated. The finite element governing equations for the dynamic analysis of the elastic body under large rotations are subsequently formulated with the aid of the constrained variational principle. A penalty parameter is introduced, and the rotational angles at element nodes are treated as independent variables to meet the requirement of C1 continuity. The elastic body is discretized through the isoparametric element with 8 nodes and 48 degrees-of-freedom. As an example with an application of the motion-deformation coupled model, the dynamic analysis on a rotating cantilever with two spatial layouts relative to the rotational axis is numerically implemented. Dynamic frequencies of the rotating cantilever are presented at prescribed constant spin velocities. The maximal rigid rotational velocity is extended for ensuring the applicability of the linear model. A complete set of dynamical response of the rotating cantilever in the case of spin-up maneuver is examined, it is shown that, under the ultimate rigid rotational velocities less than the maximal rigid rotational velocity, the stress strength may exceed the material strength tolerance even though the displacement and rotational angle responses are both convergent. The influence of the cantilever layouts on their responses and

  8. Generalized Likelihood Ratio Statistics and Uncertainty Adjustments in Efficient Adaptive Design of Clinical Trials

    CERN Document Server

    Bartroff, Jay

    2011-01-01

    A new approach to adaptive design of clinical trials is proposed in a general multiparameter exponential family setting, based on generalized likelihood ratio statistics and optimal sequential testing theory. These designs are easy to implement, maintain the prescribed Type I error probability, and are asymptotically efficient. Practical issues involved in clinical trials allowing mid-course adaptation and the large literature on this subject are discussed, and comparisons between the proposed and existing designs are presented in extensive simulation studies of their finite-sample performance, measured in terms of the expected sample size and power functions.

  9. Adaptive global synchronization of a general complex dynamical network with non-delayed and delayed coupling

    Science.gov (United States)

    Wen, Sun; Chen, Shihua; Guo, Wanli

    2008-10-01

    This Letter investigates the global synchronization of a general complex dynamical network with non-delayed and delayed coupling. Based on Lasalle's invariance principle, adaptive global synchronization criteria is obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-delayed and delayed coupling can globally asymptotically synchronize to a given trajectory. What is more, the node dynamic need not satisfy the very strong and conservative uniformly Lipschitz condition and the coupling matrix is not assumed to be symmetric or irreducible. Finally, numerical simulations are presented to verify the effectiveness of the proposed synchronization criteria.

  10. Adaptive global synchronization of a general complex dynamical network with non-delayed and delayed coupling

    Energy Technology Data Exchange (ETDEWEB)

    Wen Sun [College of Mathematics and Statistics, Wuhan University, Wuhan 430072 (China)], E-mail: sunwen_2201@163.com; Chen Shihua; Guo Wanli [College of Mathematics and Statistics, Wuhan University, Wuhan 430072 (China)

    2008-10-13

    This Letter investigates the global synchronization of a general complex dynamical network with non-delayed and delayed coupling. Based on Lasalle's invariance principle, adaptive global synchronization criteria is obtained. Analytical result shows that under the designed adaptive controllers, a general complex dynamical network with non-delayed and delayed coupling can globally asymptotically synchronize to a given trajectory. What is more, the node dynamic need not satisfy the very strong and conservative uniformly Lipschitz condition and the coupling matrix is not assumed to be symmetric or irreducible. Finally, numerical simulations are presented to verify the effectiveness of the proposed synchronization criteria.

  11. The Adapted Ordering Method for the Representation Theory of Lie Algebras and Superalgebras and their Generalizations

    CERN Document Server

    Gato-Rivera, Beatriz

    2008-01-01

    In 1998 the Adapted Ordering Method was developed for the study of the representation theory of the superconformal algebras in two dimensions. It allows: to determine the maximal dimension for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this talk I introduce the present version of the Adapted Ordering Method, published in J. Phys. A: Math. Theor. 41 (2008) 045201, which can be applied to general Lie algebras and superalgebras and their generalizations, provided they can be triangulated.

  12. The Adapted Ordering Method for Lie Algebras and Superalgebras and their Generalizations

    CERN Document Server

    Gato-Rivera, Beatriz

    2007-01-01

    In 1998 the Adapted Ordering Method was developed for the representation theory of the superconformal algebras in two dimensions. It allows: to determine maximal dimensions for a given type of space of singular vectors, to identify all singular vectors by only a few coefficients, to spot subsingular vectors and to set the basis for constructing embedding diagrams. In this article we present the Adapted Ordering Method for general Lie algebras and superalgebras, and their generalizations, provided they can be triangulated. We also review briefly the results obtained for the Virasoro algebra and for the N=2 and Ramond N=1 superconformal algebras.

  13. Adaptive current compensation with nonlinear disturbance observer for single-sided linear induction motor considering dynamic eddy-effect

    Institute of Scientific and Technical Information of China (English)

    DENG Jiang-ming; CHEN Te-fang; CHEN Chun-yang

    2015-01-01

    An adaptive current compensation control for a single-sided linear induction motor (SLIM) with nonlinear disturbance observer was developed. First, to maintaint-axis secondary component flux constant with consideration of the specially dynamic eddy-effect (DEE) of the SLIM, a instantaneously tracing compensation ofm-axis current component was analyzed. Second, adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer (NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.

  14. Regularization and improved interpretation of linear data mappings and adaptive distance measures

    NARCIS (Netherlands)

    Strickert, Marc; Hammer, Barbara; Villmann, Thomas; Biehl, Michael

    2013-01-01

    Linear data transformations are essential operations in many machine learning algorithms, helping to make such models more flexible or to emphasize certain data directions. In particular for high dimensional data sets linear transformations are not necessarily uniquely determined, though, and altern

  15. Designing Adaptive Linear Array Antenna to Achieve Pattern Steering Optimization by Phase-Amplitude Perturbations Using Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    HSUChaohsing; CHENTsongyi; PanJengshyang

    2005-01-01

    In this paper, a phase-amplitude perturbation method of an adaptive array factor based on the genetic algorithm is proposed. The design for an optimal beam pattern of an adaptive antenna is able to not only suppress interference by placing nulls at the directions of the interfering sources but also provide a maximized main lobe in the direction of the desired signal, i.e., to maximizethe Signal interference ratio (SIR). In order to achieve this goal, a kind of new convergent skill called the two-way convergent method for genetic algorithms is proposed. The phase-amplitude perturbation method is applied to realize the optimal beam pattern of an adaptively linear array antenna. The Genetic algorithms are applied to find the optimal phase-amplitude weighting vector of adaptive array factor. An optimal beam pattern of linear array is derived by phase-amplitude perturbations using a genetic algorithm. Computer simulation result is given to demonstrate the effectiveness of the proposed method.

  16. Adaptive control of linear multivariable systems with high frequency gain matrix hurwitz

    Institute of Scientific and Technical Information of China (English)

    Ying ZHOU; Yuqiang WU; Shumin FEI

    2005-01-01

    A new adaptive control scheme is proposed for multivariable model reference adaptive control(MRAC) systems based on the nonlinear backstepping approach with vector form.The assumption on a priori knowledge of the high frequency gain matrix in existing results is relaxed and the new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable.This control scheme guarantees the global stability of the closed-loop systems and the tracking error can be arbitrary small.The simulation result for an application example shows the validity of the proposed nonlinear adaptive scheme.

  17. No Evidence for a Low Linear Energy Transfer Adaptive Response in Irradiated RKO Cells

    Energy Technology Data Exchange (ETDEWEB)

    Sowa, Marianne B.; Goetz, Wilfried; Baulch, Janet E.; Lewis, Adam J.; Morgan, William F.

    2011-01-06

    It has become increasingly evident from reports in the literature that there are many confounding factors that are capable of modulating radiation induced non-targeted responses such as the bystander effect and the adaptive response. In this paper we examine recent data that suggest that the observation of non-targeted responses may not be universally observable for differing radiation qualities. We have conducted a study of the adaptive response following low LET exposures for human colon carcinoma cells and failed to observe adaption for the endpoints of clonogenic survival or micronucleus formation.

  18. The Overlooked Potential of Generalized Linear Models in Astronomy-II: Gamma regression and photometric redshifts

    CERN Document Server

    Elliott, J; Krone-Martins, A; Cameron, E; Ishida, E E O; Hilbe, J

    2014-01-01

    Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the photo-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ~1% for simulated and ~2% for...

  19. Generalized linear model for mapping discrete trait loci implemented with LASSO algorithm.

    Directory of Open Access Journals (Sweden)

    Jun Xing

    Full Text Available Generalized estimating equation (GEE algorithm under a heterogeneous residual variance model is an extension of the iteratively reweighted least squares (IRLS method for continuous traits to discrete traits. In contrast to mixture model-based expectation-maximization (EM algorithm, the GEE algorithm can well detect quantitative trait locus (QTL, especially large effect QTLs located in large marker intervals in the manner of high computing speed. Based on a single QTL model, however, the GEE algorithm has very limited statistical power to detect multiple QTLs because of ignoring other linked QTLs. In this study, the fast least absolute shrinkage and selection operator (LASSO is derived for generalized linear model (GLM with all possible link functions. Under a heterogeneous residual variance model, the LASSO for GLM is used to iteratively estimate the non-zero genetic effects of those loci over entire genome. The iteratively reweighted LASSO is therefore extended to mapping QTL for discrete traits, such as ordinal, binary, and Poisson traits. The simulated and real data analyses are conducted to demonstrate the efficiency of the proposed method to simultaneously identify multiple QTLs for binary and Poisson traits as examples.

  20. Criteria for the Single-Valued Metric Generalized Inverses of Multi-Valued Linear Operators in Banach Spaces

    Institute of Scientific and Technical Information of China (English)

    Yu Wen WANG; Jian ZHANG; Yun An CUI

    2012-01-01

    Let X,Y be Banach spaces and M be a linear subspace in X × Y ={{x,y}|x ∈ X,y ∈ Y}.We may view M as a multi-valued linear operator from X to Y by taking M(x) ={y|{x,y} ∈ M}.In this paper,we give several criteria for a single-valued operator from Y to X to be the metric generalized inverse of the multi-valued linear operator M.The principal tool in this paper is also the generalized orthogonal decomposition theorem in Banach spaces.

  1. A multichannel nonlinear adaptive noise canceller based on generalized FLANN for fetal ECG extraction

    Science.gov (United States)

    Ma, Yaping; Xiao, Yegui; Wei, Guo; Sun, Jinwei

    2016-01-01

    In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TSpca) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times more

  2. Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

    Science.gov (United States)

    Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

    2016-05-01

    Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .

  3. A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

    Directory of Open Access Journals (Sweden)

    Ana Calabrese

    Full Text Available In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF, a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM. In this model, each cell's input is described by: 1 a stimulus filter (STRF; and 2 a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs and modulation limited (ml noise. We compare this model to normalized reverse correlation (NRC, the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.

  4. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

    2009-12-28

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  5. Non-linear adaptive phenomena which decrease the risk of infection after pre-exposure to radiofrequency radiation.

    Science.gov (United States)

    Mortazavi, S M J; Motamedifar, M; Namdari, G; Taheri, M; Mortazavi, A R; Shokrpour, N

    2014-05-01

    Substantial evidence indicates that adaptive response induced by low doses of ionizing radiation can result in resistance to the damage caused by a subsequently high-dose radiation or cause cross-resistance to other non-radiation stressors. Adaptive response contradicts the linear-non-threshold (LNT) dose-response model for ionizing radiation. We have previously reported that exposure of laboratory animals to radiofrequency radiation can induce a survival adaptive response. Furthermore, we have indicated that pre-exposure of mice to radiofrequency radiation emitted by a GSM mobile phone increased their resistance to a subsequent Escherichia coli infection. In this study, the survival rates in animals receiving both adapting (radiofrequency) and challenge dose (bacteria) and the animals receiving only the challenge dose (bacteria) were 56% and 20%, respectively. In this light, our findings contribute to the assumption that radiofrequency-induced adaptive response can be used as an efficient method for decreasing the risk of infection in immunosuppressed irradiated individuals. The implication of this phenomenon in human's long term stay in the space is also discussed.

  6. Robust adaptive synchronization of general dynamical networks with multiple delays and uncertainties

    Indian Academy of Sciences (India)

    LU YIMING; HE PING; MA SHU-HUA; LI GUO-ZHI; MOBAYBEN SALEH

    2016-06-01

    In this article, a general complex dynamical network which contains multiple delays and uncertainties is introduced, which contains time-varying coupling delays, time-varying node delay, and uncertainties of both the inner- and outer-coupling matrices. A robust adaptive synchronization scheme for these general complex networks with multiple delays and uncertainties is established and raised by employing the robust adaptive control principle and the Lyapunov stability theory. We choose some suitable adaptive synchronization controllers to ensure the robust synchronization of this dynamical network. The numerical simulations of the time-delay Lorenz chaotic system as local dynamical node are provided to observe and verify the viability and productivity of the theoretical research in this paper. Compared to the achievement of previous research, theresearch in this paper seems quite comprehensive and universal.

  7. Adaptive generalized combination complex synchronization of uncertain real and complex nonlinear systems

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Shi-bing, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn [School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236041 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xing-yuan, E-mail: wang-shibing@dlut.edu.cn, E-mail: wangxy@dlut.edu.cn [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xiu-you [School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236041 (China); Zhou, Yu-fei [College of Electrical Engineering and Automation, Anhui University, Hefei 230601 (China)

    2016-04-15

    With comprehensive consideration of generalized synchronization, combination synchronization and adaptive control, this paper investigates a novel adaptive generalized combination complex synchronization (AGCCS) scheme for different real and complex nonlinear systems with unknown parameters. On the basis of Lyapunov stability theory and adaptive control, an AGCCS controller and parameter update laws are derived to achieve synchronization and parameter identification of two real drive systems and a complex response system, as well as two complex drive systems and a real response system. Two simulation examples, namely, ACGCS for chaotic real Lorenz and Chen systems driving a hyperchaotic complex Lü system, and hyperchaotic complex Lorenz and Chen systems driving a real chaotic Lü system, are presented to verify the feasibility and effectiveness of the proposed scheme.

  8. Multichannel Speech Enhancement Based on Generalized Gamma Prior Distribution with Its Online Adaptive Estimation

    Science.gov (United States)

    Dat, Tran Huy; Takeda, Kazuya; Itakura, Fumitada

    We present a multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online adaptive estimation is shown to be effective for speech spectral estimation in noisy environments. Furthermore, the multi-channel information in terms of cross-channel statistics are shown to be useful to better adapt the prior distribution parameters to the actual observation, resulting in better performance of speech enhancement algorithm. We tested the proposed algorithm in an in-car speech database and obtained significant improvements of the speech recognition performance, particularly under non-stationary noise conditions such as music, air-conditioner and open window.

  9. Considering the Use of General and Modified Assessment Items in Computerized Adaptive Testing

    Science.gov (United States)

    Wyse, Adam E.; Albano, Anthony D.

    2015-01-01

    This article used several data sets from a large-scale state testing program to examine the feasibility of combining general and modified assessment items in computerized adaptive testing (CAT) for different groups of students. Results suggested that several of the assumptions made when employing this type of mixed-item CAT may not be met for…

  10. Adaptive Feedback Linearization Control for Asynchronous Machine with Nonlinear for Natural Dynamic Complete Observer

    Science.gov (United States)

    Bentaallah, Abderrahim; Massoum, Ahmed; Benhamida, Farid; Meroufel, Abdelkader

    2012-03-01

    This paper studies the nonlinear adaptive control of an induction motor with natural dynamic complete nonlinear observer. The aim of this work is to develop a nonlinear control law and adaptive performance for an asynchronous motor with two main objectives: to improve the continuation of trajectories and the stability, robustness to parametric variations and disturbances rejection. This control law will independently control the speed and flux into the machine by restricting supply. A complete nonlinear observer for dynamic nature ensuring closed loop stability of the entire control and observer has been developed. Several simulations have also been carried out to demonstrate system performance.

  11. A Blind Equalizer Based on Unsupervised Gaussian Cluster Formation with an Adaptive Non—Linearity

    Institute of Scientific and Technical Information of China (English)

    LiuHanyu; TongWen; 等

    1997-01-01

    In this paper we present a blind equalizer algorithm based on an unsupervised Gaussian cluster formation technique with an optimized gradient adaptive step-size to update the equalizer coefficients.The novelty of this work lies in the optimization of the nonlinearity of the cluster formation used to achieve an optimal soft decision.The proposed iterative procedure combined with the variable step-size gradient-based adaptation,significantly accelerates the convergence speed of the blind equalization.The advantages of the proposed equalization techniques are illustrated by simulation.Simulation results obtained are compared with the Sato and Godard blind equalizers.

  12. MGMRES: A generalization of GMRES for solving large sparse nonsymmetric linear systems

    Energy Technology Data Exchange (ETDEWEB)

    Young, D.M.; Chen, J.Y. [Univ. of Texas, Austin, TX (United States)

    1994-12-31

    The authors are concerned with the solution of the linear system (1): Au = b, where A is a real square nonsingular matrix which is large, sparse and non-symmetric. They consider the use of Krylov subspace methods. They first choose an initial approximation u{sup (0)} to the solution {bar u} = A{sup {minus}1}B of (1). They also choose an auxiliary matrix Z which is nonsingular. For n = 1,2,{hor_ellipsis} they determine u{sup (n)} such that u{sup (n)} {minus} u{sup (0)}{epsilon}K{sub n}(r{sup (0)},A) where K{sub n}(r{sup (0)},A) is the (Krylov) subspace spanned by the Krylov vectors r{sup (0)}, Ar{sup (0)}, {hor_ellipsis}, A{sup n{minus}1}r{sup 0} and where r{sup (0)} = b{minus}Au{sup (0)}. If ZA is SPD they also require that (u{sup (n)}{minus}{bar u}, ZA(u{sup (n)}{minus}{bar u})) be minimized. If, on the other hand, ZA is not SPD, then they require that the Galerkin condition, (Zr{sup n}, v) = 0, be satisfied for all v{epsilon}K{sub n}(r{sup (0)}, A) where r{sup n} = b{minus}Au{sup (n)}. In this paper the authors consider a generalization of GMRES. This generalized method, which they refer to as `MGMRES`, is very similar to GMRES except that they let Z = A{sup T}Y where Y is a nonsingular matrix which is symmetric by not necessarily SPD.

  13. c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

    Directory of Open Access Journals (Sweden)

    Martin Sill

    2014-12-01

    Full Text Available We have developed the R package c060 with the aim of improving R software func- tionality for high-dimensional risk prediction modeling, e.g., for prognostic modeling of survival data using high-throughput genomic data. Penalized regression models provide a statistically appealing way of building risk prediction models from high-dimensional data. The popular CRAN package glmnet implements an efficient algorithm for fitting penalized Cox and generalized linear models. However, in practical applications the data analysis will typically not stop at the point where the model has been fitted. One is for example often interested in the stability of selected features and in assessing the prediction performance of a model and we provide functions to deal with both of these tasks. Our R functions are computationally efficient and offer the possibility of speeding up computing time through parallel computing. Another feature which can drastically reduce computing time is an efficient interval-search algorithm, which we have implemented for selecting the optimal parameter combination for elastic net penalties. These functions have been useful in our daily work at the Biostatistics department (C060 of the German Cancer Research Center where prognostic modeling of patient survival data is of particular interest. Although we focus on a survival data application of penalized Cox models in this article, the functions in our R package are in general applicable to all types of regression models implemented in the glmnet package, with the exception of prediction error curves, which are specific to time-to-event data.

  14. The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

    Science.gov (United States)

    Elliott, J.; de Souza, R. S.; Krone-Martins, A.; Cameron, E.; Ishida, E. E. O.; Hilbe, J.

    2015-04-01

    Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ∼1% for simulated and ∼2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.

  15. Fast inference in generalized linear models via expected log-likelihoods.

    Science.gov (United States)

    Ramirez, Alexandro D; Paninski, Liam

    2014-04-01

    Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates; the resulting "expected log-likelihood" can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the distribution over model covariates is controlled by the experimenter and the expected log-likelihood approximation becomes particularly useful; for example, estimators based on maximizing this expected log-likelihood (or a penalized version thereof) can often be obtained with orders of magnitude computational savings compared to the exact maximum likelihood estimators. A risk analysis establishes that these maximum EL estimators often come with little cost in accuracy (and in some cases even improved accuracy) compared to standard maximum likelihood estimates. Finally, we find that these methods can significantly decrease the computation time of marginal likelihood calculations for model selection and of Markov chain Monte Carlo methods for sampling from the posterior parameter distribution. We illustrate our results by applying these methods to a computationally-challenging dataset of neural spike trains obtained via large-scale multi-electrode recordings in the primate retina.

  16. Setting a generalized functional linear model (GFLM for the classification of different types of cancer

    Directory of Open Access Journals (Sweden)

    Miguel Flores

    2016-11-01

    Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.

  17. Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

    Science.gov (United States)

    Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G

    2016-09-01

    A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.

  18. Statistical Methods for Quality Control of Steel Coils Manufacturing Process using Generalized Linear Models

    Science.gov (United States)

    García-Díaz, J. Carlos

    2009-11-01

    Fault detection and diagnosis is an important problem in process engineering. Process equipments are subject to malfunctions during operation. Galvanized steel is a value added product, furnishing effective performance by combining the corrosion resistance of zinc with the strength and formability of steel. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing and the increasingly stringent quality requirements in automotive industry has also demanded ongoing efforts in process control to make the process more robust. When faults occur, they change the relationship among these observed variables. This work compares different statistical regression models proposed in the literature for estimating the quality of galvanized steel coils on the basis of short time histories. Data for 26 batches were available. Five variables were selected for monitoring the process: the steel strip velocity, four bath temperatures and bath level. The entire data consisting of 48 galvanized steel coils was divided into sets. The first training data set was 25 conforming coils and the second data set was 23 nonconforming coils. Logistic regression is a modeling tool in which the dependent variable is categorical. In most applications, the dependent variable is binary. The results show that the logistic generalized linear models do provide good estimates of quality coils and can be useful for quality control in manufacturing process.

  19. Determination of a Differential Item Functioning Procedure Using the Hierarchical Generalized Linear Model

    Directory of Open Access Journals (Sweden)

    Tülin Acar

    2012-01-01

    Full Text Available The aim of this research is to compare the result of the differential item functioning (DIF determining with hierarchical generalized linear model (HGLM technique and the results of the DIF determining with logistic regression (LR and item response theory–likelihood ratio (IRT-LR techniques on the test items. For this reason, first in this research, it is determined whether the students encounter DIF with HGLM, LR, and IRT-LR techniques according to socioeconomic status (SES, in the Turkish, Social Sciences, and Science subtest items of the Secondary School Institutions Examination. When inspecting the correlations among the techniques in terms of determining the items having DIF, it was discovered that there was significant correlation between the results of IRT-LR and LR techniques in all subtests; merely in Science subtest, the results of the correlation between HGLM and IRT-LR techniques were found significant. DIF applications can be made on test items with other DIF analysis techniques that were not taken to the scope of this research. The analysis results, which were determined by using the DIF techniques in different sample sizes, can be compared.

  20. Motor adaptation and generalization of reaching movements using motor primitives based on spatial coordinates.

    Science.gov (United States)

    Tanaka, Hirokazu; Sejnowski, Terrence J

    2015-02-15

    The brain processes sensory and motor information in a wide range of coordinate systems, ranging from retinal coordinates in vision to body-centered coordinates in areas that control musculature. Here we focus on the coordinate system used in the motor cortex to guide actions and examine physiological and psychophysical evidence for an allocentric reference frame based on spatial coordinates. When the equations of motion governing reaching dynamics are expressed as spatial vectors, each term is a vector cross product between a limb-segment position and a velocity or acceleration. We extend this computational framework to motor adaptation, in which the cross-product terms form adaptive bases for canceling imposed perturbations. Coefficients of the velocity- and acceleration-dependent cross products are assumed to undergo plastic changes to compensate the force-field or visuomotor perturbations. Consistent with experimental findings, each of the cross products had a distinct reference frame, which predicted how an acquired remapping generalized to untrained location in the workspace. In response to force field or visual rotation, mainly the coefficients of the velocity- or acceleration-dependent cross products adapted, leading to transfer in an intrinsic or extrinsic reference frame, respectively. The model further predicted that remapping of visuomotor rotation should under- or overgeneralize in a distal or proximal workspace. The cross-product bases can explain the distinct patterns of generalization in visuomotor and force-field adaptation in a unified way, showing that kinematic and dynamic motor adaptation need not arise through separate neural substrates.

  1. General Acid Catalysis: A Flexible Experiment, Adaptable to Student Ability and Various Teaching Approaches.

    Science.gov (United States)

    Bulmer, R. S.; And Others

    1981-01-01

    The acid-catalyzed hydrolysis of N-vinyl pyrrolidone provides a simple spectrophotometric kinetic experiment to introduce general acid catalysis, solvent isotope effects, and other aspects of ionic reactions in solution in advanced courses. The Bronsted equation and concept of linear free-energy changes is also covered. (SK)

  2. Supporting Dynamic Adaptive Streaming over HTTP in Wireless Meshed Networks using Random Linear Network Coding

    DEFF Research Database (Denmark)

    Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani

    2014-01-01

    This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...

  3. A NEW SELF-ADAPTIVE ITERATIVE METHOD FOR GENERAL MIXED QUASI VARIATIONAL INEQUALITIES

    Institute of Scientific and Technical Information of China (English)

    Abdellah Bnouhachem; Mohamed Khalfaoui; Hafida Benazza

    2008-01-01

    The general mixed quasi variational inequality containing a nonlinear term ψ is a useful and an important generalization of variational inequalities. The projection method can not be applied to solve this problem due to the presence of nonlinear term. It is well known that the variational inequalities involving the nonlinear term ψ are equivalent to the fixed point problems and re, solvent equations. In this article, the authors use these alternative equivalent formulations to suggest and analyze a new self-adaptive iterative method for solving general mixed quasi variational inequalities. Global convergence of the new method is proved. An example is given to illustrate the efficiency of the proposed method.

  4. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

    Directory of Open Access Journals (Sweden)

    Chuan He

    2014-01-01

    Full Text Available We propose an adaptive total generalized variation (TGV based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS and the classical augmented Lagrangian method (ALM are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.

  5. Assessing the tangent linear behaviour of common tracer transport schemes and their use in a linearised atmospheric general circulation model

    Directory of Open Access Journals (Sweden)

    Daniel Holdaway

    2015-09-01

    Full Text Available The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5. All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have non-linear behaviour. The piecewise parabolic method (PPM with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.

  6. Adaptive Kronrod-Patterson integration of non-linear finite-element matrices

    DEFF Research Database (Denmark)

    Janssen, Hans

    2010-01-01

    Efficient simulation of unsaturated moisture flow in porous media is of great importance in many engineering fields. The highly non-linear character of unsaturated flow typically gives sharp moving moisture fronts during wetting and drying of materials with strong local moisture permeability and ...

  7. Continuous dependence of solutions of abstract generalized linear differential equations with potential converging uniformly with a weight

    OpenAIRE

    Monteiro, G.; Tvrdý, M. (Milan)

    2014-01-01

    In this paper we continue our research on continuous dependence on a parameter of solutions to generalized linear differential equations. These equations are described by linear integral equations containing the abstract Kurzweil-Stieltjes integral. In particular, we are interested in the situation when the kernels of these equations need not have uniformly bounded variations. Our main goal is the extension of our previous results to the nonhomogeneous case. Applications to second order syste...

  8. The Relationship between Two Kinds of Generalized Convex Set-Valued Maps in Real Ordered Linear Spaces

    Directory of Open Access Journals (Sweden)

    Zhi-Ang Zhou

    2013-01-01

    Full Text Available A new notion of the ic-cone convexlike set-valued map characterized by the algebraic interior and the vector closure is introduced in real ordered linear spaces. The relationship between the ic-cone convexlike set-valued map and the nearly cone subconvexlike set-valued map is established. The results in this paper generalize some known results in the literature from locally convex spaces to linear spaces.

  9. Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

    Science.gov (United States)

    Fu, Yue; Fu, Jun; Chai, Tianyou

    2015-12-01

    In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

  10. A smart rotor configuration with linear quadratic control of adaptive trailing edge flaps for active load alleviation

    DEFF Research Database (Denmark)

    Bergami, Leonardo; Poulsen, Niels Kjølstad

    2015-01-01

    The paper proposes a smart rotor configuration where adaptive trailing edge flaps (ATEFs) are employed for active alleviation of the aerodynamic loads on the blades of the NREL 5 MW reference turbine. The flaps extend for 20% of the blade length and are controlled by a linear quadratic (LQ....... The effects of active flap control are assessed with aeroelastic simulations of the turbine in normal operation conditions, as prescribed by the International Electrotechnical Commission standard. The turbine lifetime fatigue damage equivalent loads provide a convenient summary of the results achieved...

  11. Adaptive neural control of non-affine pure-feedback non-linear systems with input nonlinearity and perturbed uncertainties

    Science.gov (United States)

    Zhang, Tian-Ping; Zhu, Qing; Yang, Yue-Quan

    2012-04-01

    In this article, two robust adaptive control schemes are investigated for a class of completely non-affine pure-feedback non-linear systems with input non-linearity and perturbed uncertainties using radial basis function neural networks (RBFNNs). Based on the dynamic surface control (DSC) technique and using the quadratic Lyapunov function, the explosion of complexity in the traditional backstepping design is avoided when the gain signs are known. In addition, the unknown virtual gain signs are dealt with using the Nussbaum functions. Using the mean value theorem and Young's inequality, only one learning parameter needs to be tuned online at each step of recursion. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.

  12. A General Framework for Sequential and Adaptive Methods in Survival Studies

    CERN Document Server

    Luo, Xiaolong; Ying, Zhiliang

    2011-01-01

    Adaptive treatment allocation schemes based on interim responses have generated a great deal of recent interest in clinical trials and other follow-up studies. An important application of such schemes is in survival studies, where the response variable of interest is time to the occurrence of a certain event. Due to possible dependency structures inherited from the enrollment and allocation schemes, existing approaches to survival models, including those that handle staggered entry, cannot be applied directly. This paper develops a new general framework with its theoretical foundation for handling such adaptive designs. The new approach is based on marked point processes and differs from existing approaches in that it considers entry and calender times rather than survival and calender times. Large sample properties, which are essential for statistical inference, are established. Special attention is given to the Cox model and related score processes. Applications to adaptive and sequential designs are discus...

  13. Predicting stem borer density in maize using RapidEye data and generalized linear models

    Science.gov (United States)

    Abdel-Rahman, Elfatih M.; Landmann, Tobias; Kyalo, Richard; Ong'amo, George; Mwalusepo, Sizah; Sulieman, Saad; Ru, Bruno Le

    2017-05-01

    Average maize yield in eastern Africa is 2.03 t ha-1 as compared to global average of 6.06 t ha-1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson ('Po') and negative binomial ('NB') distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB ('ZINB') models outperformed the 'NB' models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69-1.06 and RPD = 8.25-19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.

  14. Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.

    Science.gov (United States)

    Fan, Ruzong; Wang, Yifan; Chiu, Chi-Yang; Chen, Wei; Ren, Haobo; Li, Yun; Boehnke, Michael; Amos, Christopher I; Moore, Jason H; Xiong, Momiao

    2016-02-01

    We developed generalized functional linear models (GFLMs) to perform a meta-analysis of multiple case-control studies to evaluate the relationship of genetic data to dichotomous traits adjusting for covariates. Unlike the previously developed meta-analysis for sequence kernel association tests (MetaSKATs), which are based on mixed-effect models to make the contributions of major gene loci random, GFLMs are fixed models; i.e., genetic effects of multiple genetic variants are fixed. Based on GFLMs, we developed chi-squared-distributed Rao's efficient score test and likelihood-ratio test (LRT) statistics to test for an association between a complex dichotomous trait and multiple genetic variants. We then performed extensive simulations to evaluate the empirical type I error rates and power performance of the proposed tests. The Rao's efficient score test statistics of GFLMs are very conservative and have higher power than MetaSKATs when some causal variants are rare and some are common. When the causal variants are all rare [i.e., minor allele frequencies (MAF) analysis of eight European studies and detected significant association for 18 genes (P < 3.10 × 10(-6)), tentative association for 2 genes (HHEX and HMGA2; P ≈ 10(-5)), and no association for 2 genes, while MetaSKATs detected none. In addition, the traditional additive-effect model detects association at gene HHEX. GFLMs and related tests can analyze rare or common variants or a combination of the two and can be useful in whole-genome and whole-exome association studies.

  15. Generalized functional linear models for gene-based case-control association studies.

    Science.gov (United States)

    Fan, Ruzong; Wang, Yifan; Mills, James L; Carter, Tonia C; Lobach, Iryna; Wilson, Alexander F; Bailey-Wilson, Joan E; Weeks, Daniel E; Xiong, Momiao

    2014-11-01

    By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.

  16. A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints.

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, Robert Michael (College of William and Mary, Williamsburg, VA); Torczon, Virginia Joanne (College of William and Mary, Williamsburg, VA); Kolda, Tamara Gibson

    2006-08-01

    We consider the solution of nonlinear programs in the case where derivatives of the objective function and nonlinear constraints are unavailable. To solve such problems, we propose an adaptation of a method due to Conn, Gould, Sartenaer, and Toint that proceeds by approximately minimizing a succession of linearly constrained augmented Lagrangians. Our modification is to use a derivative-free generating set direct search algorithm to solve the linearly constrained subproblems. The stopping criterion proposed by Conn, Gould, Sartenaer and Toint for the approximate solution of the subproblems requires explicit knowledge of derivatives. Such information is presumed absent in the generating set search method we employ. Instead, we show that stationarity results for linearly constrained generating set search methods provide a derivative-free stopping criterion, based on a step-length control parameter, that is sufficient to preserve the convergence properties of the original augmented Lagrangian algorithm.

  17. Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

    CERN Document Server

    Frederic, Schmidt; Stephane, Le Mouelic

    2014-01-01

    Minerals detection over large volume of spectra is the challenge addressed by current hyperspectral imaging spectrometer in Planetary Science. Instruments such OMEGA (Mars Express), CRISM (Mars Reconnaissance Orbiter), M^{3} (Chandrayaan-1), VIRTIS (Rosetta) and many more, have been producing very large datasets since one decade. We propose here a fast supervised detection algorithm called LinMin, in the framework of linear unmixing, with innovative arrangement in order to treat non-linear cases due to radiative transfer in both atmosphere and surface. We use reference laboratory and synthetic spectral library. Additional spectra are used in order to mimic the effect of Martian aerosols, grain size, and observation geometry discrepancies between reference and observed spectra. The proposed algorithm estimates the uncertainty on mixing coefficient from the uncertainty of observed spectra. Both numerical and observational tests validate the approach. Fast parallel implementation of the best algorithm (IPLS) on ...

  18. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Winston Y. [Univ. of California, Berkeley, CA (United States)

    1993-04-01

    This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.

  19. Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

    CERN Document Server

    Jackson, Kate; Lardiere, Olivier; Andersen, Dave; Bradley, Colin

    2015-01-01

    We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behaviour of the previous results under less ideal conditions. Results show that +/- 100pc wind-speed error and +/- 50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.

  20. Deriving robust and globalized robust solutions of uncertain linear programs having general convex uncertainty sets

    NARCIS (Netherlands)

    Gorissen, B.L.; Blanc, J.P.C.; den Hertog, D.; Ben-Tal, A.

    We propose a new way to derive tractable robust counterparts of a linear program based on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First we obtain a new convex reformulation of the dual problem of a robust linear program, and then show how to construct

  1. Adaptions of ArcGIS' Linear Referencing System to the Coastal Environment

    DEFF Research Database (Denmark)

    Balstrøm, Thomas

    2008-01-01

    For many years it has been problematic to store information for the coastal environment in a GIS. However, a system named "Linear referencing System" based upon a dynamic segmentation principle implemented in ESRIs ArcGIS 9 software has now made it possible to store and analyze information refere...... referenced to the coastline. This presentation  demonstrates how to initialize this system, how to incorporate data into it and how to perform analysis queries hereon....

  2. Assessing the Tangent Linear Behaviour of Common Tracer Transport Schemes and Their Use in a Linearised Atmospheric General Circulation Model

    Science.gov (United States)

    Holdaway, Daniel; Kent, James

    2015-01-01

    The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.

  3. Assessing the Tangent Linear Behaviour of Common Tracer Transport Schemes and Their Use in a Linearised Atmospheric General Circulation Model

    Science.gov (United States)

    Holdaway, Daniel; Kent, James

    2015-01-01

    The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.

  4. Lin-Kernighan Heuristic Adaptation for the Generalized Traveling Salesman Problem

    CERN Document Server

    Karapetyan, Daniel

    2010-01-01

    Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. However, it was never applied to the the Generalized Traveling Salesman Problem (GTSP) though it has the same nature as TSP. In this paper we discuss possible adaptations of TSP heuristics for GTSP and focus on the case of Lin-Kernighan algorithm. At first, we provide an easy-to-understand description of the original Lin-Kernighan heuristic. Then we propose several adaptations, both trivial and complicated ones. Finally, we conduct a fair competition between all the variations of Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our adaptation of Lin-Kernighan algorithm for GTSP reproduces the success of the original heuristic. Different variations of our adaptation outperform all other heuristics in a wide range of tradeoffs between solution quality and running time, making Lin-Kernighan the state...

  5. Impact of co-channel interference on the performance of adaptive generalized transmit beamforming

    KAUST Repository

    Radaydeh, Redha Mahmoud Mesleh

    2011-08-01

    The impact of co-channel interference on the performance of adaptive generalized transmit beamforming for low-complexity multiple-input single-output (MISO) configuration is investigated. The transmit channels are assumed to be sufficiently separated and undergo Rayleigh fading conditions. Due to the limited space, a single receive antenna is employed to capture desired user transmission. The number of active transmit channels is adjusted adaptively based on statistically unordered and/or ordered instantaneous signal-to-noise ratios (SNRs), where the transmitter has no information about the statistics of undesired signals. The adaptation thresholds are identified to guarantee a target performance level, and the adaptation schemes with enhanced spectral efficiency or power efficiency are studied and their performance are compared under various channels conditions. To facilitate comparison studies, results for the statistics of instantaneous combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. The statistics for combined SNR and combined SINR are then used to quantify various performance measures, considering the impact of non-ideal estimation of the desired user channel state information (CSI) and the randomness in the number of active interferers. Numerical and simulation comparisons for the achieved performance of the adaptation schemes are presented. © 2006 IEEE.

  6. Adaptive-Anisotropic Wavelet Collocation Method on general curvilinear coordinate systems

    Science.gov (United States)

    Brown-Dymkoski, Eric; Vasilyev, Oleg V.

    2017-03-01

    A new general framework for an Adaptive-Anisotropic Wavelet Collocation Method (A-AWCM) for the solution of partial differential equations is developed. This proposed framework addresses two major shortcomings of existing wavelet-based adaptive numerical methodologies, namely the reliance on a rectangular domain and the "curse of anisotropy", i.e. drastic over-resolution of sheet- and filament-like features arising from the inability of the wavelet refinement mechanism to distinguish highly correlated directional information in the solution. The A-AWCM addresses both of these challenges by incorporating coordinate transforms into the Adaptive Wavelet Collocation Method for the solution of PDEs. The resulting integrated framework leverages the advantages of both the curvilinear anisotropic meshes and wavelet-based adaptive refinement in a complimentary fashion, resulting in greatly reduced cost of resolution for anisotropic features. The proposed Adaptive-Anisotropic Wavelet Collocation Method retains the a priori error control of the solution and fully automated mesh refinement, while offering new abilities through the flexible mesh geometry, including body-fitting. The new A-AWCM is demonstrated for a variety of cases, including parabolic diffusion, acoustic scattering, and unsteady external flow.

  7. Toward a general theory of adaptive radiation: insights from microbial experimental evolution.

    Science.gov (United States)

    Kassen, Rees

    2009-06-01

    The history of life has been punctuated by unusually spectacular periods of evolutionary diversification called adaptive radiation. Darwin's finches in the Galapagos, cichlid fishes in African Rift and Nicaraguan crater lakes, and the emergence of mammals at the end of the Cretaceous are hallmark examples. Although we have learned much from these and other case studies about the mechanisms thought to drive adaptive radiations, convincing experimental tests of theory are often lacking for the simple reason that it is usually impossible to "rewind the tape of life," as Stephen Jay Gould was fond of saying, and run it again. This situation has changed dramatically in recent years with the increasing emphasis on the use of microbial populations which, because of their small size and rapid generation times, make possible the construction of replicated, manipulative experiments to study evolution in the laboratory. Here I review the contributions that microbial experimental evolution has made to our understanding of the ecological and genetic mechanisms underlying adaptive radiation. I focus on three major gaps in the theory of adaptive radiation--the paucity of direct tests of mechanism, the genetics of diversification, and the limits and constraints on the progress of radiations--with the aim of pointing the way toward the development of a more general theory of adaptive radiation.

  8. A Memristor-Based Hyperchaotic Complex Lü System and Its Adaptive Complex Generalized Synchronization

    Directory of Open Access Journals (Sweden)

    Shibing Wang

    2016-02-01

    Full Text Available This paper introduces a new memristor-based hyperchaotic complex Lü system (MHCLS and investigates its adaptive complex generalized synchronization (ACGS. Firstly, the complex system is constructed based on a memristor-based hyperchaotic real Lü system, and its properties are analyzed theoretically. Secondly, its dynamical behaviors, including hyperchaos, chaos, transient phenomena, as well as periodic behaviors, are explored numerically by means of bifurcation diagrams, Lyapunov exponents, phase portraits, and time history diagrams. Thirdly, an adaptive controller and a parameter estimator are proposed to realize complex generalized synchronization and parameter identification of two identical MHCLSs with unknown parameters based on Lyapunov stability theory. Finally, the numerical simulation results of ACGS and its applications to secure communication are presented to verify the feasibility and effectiveness of the proposed method.

  9. Dynamic Wavelength and Bandwidth Allocation Using Adaptive Linear Prediction in WDM/TDM Ethernet Passive Optical Networks

    Institute of Scientific and Technical Information of China (English)

    LU Yi-yi; GUO Yong; HE Chen

    2009-01-01

    Hybrid wavelength-division-multiplexing (WDM)/time-division-multiplexing (TDM) ethernet passive optical networks (EPONs) can achieve low per-subscriber cost and scalability to increase the number of subscribers. This paper discusses dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM EPONs. Based on the correlation structure of the variable bit rate (VBR) video traffic, we propose a quality-of-service (QoS) supported DWBA using adaptive linear traffic prediction. Wavelength and timeslot are allocated dynamically by optical line terminal (OLT) to all optical network units (ONUs) based on the bandwidth requests and the guaranteed service level agreements (SLA) of all ONUs. Mean square error of the predicted average arriv-ing rate of compound video traffic during waiting period is minimized through Wiener-Hopf equation. Simulation results show that the DWBA-adaptive-linear-prediction (DWBA-ALP) algorithm can significantly improve the QoS performances in terms of low delay and high bandwidth utilization.

  10. Comparative Analysis of Linear and Nonlinear Pattern Synthesis of Hemispherical Antenna Array Using Adaptive Evolutionary Techniques

    Directory of Open Access Journals (Sweden)

    K. R. Subhashini

    2014-01-01

    synthesis is termed as the variation in the element excitation amplitude and nonlinear synthesis is process of variation in element angular position. Both ADE and AFA are a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. These methods are used to determine a set of parameters of antenna elements that provide the desired radiation pattern. The effectiveness of the algorithms for the design of conformal antenna array is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that nonlinear synthesis, aided by the discussed techniques, provides considerable enhancements compared to linear synthesis.

  11. Adaptive adjustment of the generalization-discrimination balance in larval Drosophila.

    Science.gov (United States)

    Mishra, Dushyant; Louis, Matthieu; Gerber, Bertram

    2010-09-01

    Learnt predictive behavior faces a dilemma: predictive stimuli will never 'replay' exactly as during the learning event, requiring generalization. In turn, minute differences can become meaningful, prompting discrimination. To provide a study case for an adaptive adjustment of this generalization-discrimination balance, the authors ask whether Drosophila melanogaster larvae are able to either generalize or discriminate between two odors (1-octen-3-ol and 3-octanol), depending on the task. The authors find that after discriminatively rewarding one but not the other odor, larvae show conditioned preference for the rewarded odor. On the other hand, no odor specificity is observed after nondiscriminative training, even if the test involves a choice between both odors. Thus, for this odor pair at least, discrimination training is required to confer an odor-specific memory trace. This requires that there is at least some difference in processing between the two odors already at the beginning of the training. Therefore, as a default, there is a small yet salient difference in processing between 1-octen-3-ol and 3-octanol; this difference is ignored after nondiscriminative training (generalization), whereas it is accentuated by odor-specific reinforcement (discrimination). Given that, as the authors show, both faculties are lost in anosmic Or83b(1) mutants, this indicates an adaptive adjustment of the generalization-discrimination balance in larval Drosophila, taking place downstream of Or83b-expressing sensory neurons.

  12. High Order A-stable Continuous General Linear Methods for Solution of Systems of Initial Value Problems in ODEs

    Directory of Open Access Journals (Sweden)

    Dauda GuliburYAKUBU

    2012-12-01

    Full Text Available Accurate solutions to initial value systems of ordinary differential equations may be approximated efficiently by Runge-Kutta methods or linear multistep methods. Each of these has limitations of one sort or another. In this paper we consider, as a middle ground, the derivation of continuous general linear methods for solution of stiff systems of initial value problems in ordinary differential equations. These methods are designed to combine the advantages of both Runge-Kutta and linear multistep methods. Particularly, methods possessing the property of A-stability are identified as promising methods within this large class of general linear methods. We show that the continuous general linear methods are self-starting and have more ability to solve the stiff systems of ordinary differential equations, than the discrete ones. The initial value systems of ordinary differential equations are solved, for instance, without looking for any other method to start the integration process. This desirable feature of the proposed approach leads to obtaining very high accuracy of the solution of the given problem. Illustrative examples are given to demonstrate the novelty and reliability of the methods.

  13. Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.

    Science.gov (United States)

    Niemi, Jarad; West, Mike

    2010-06-01

    We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.

  14. Power Allocation Optimization: Linear Precoding Adapted to NB-LDPC Coded MIMO Transmission

    Directory of Open Access Journals (Sweden)

    Tarek Chehade

    2015-01-01

    Full Text Available In multiple-input multiple-output (MIMO transmission systems, the channel state information (CSI at the transmitter can be used to add linear precoding to the transmitted signals in order to improve the performance and the reliability of the transmission system. This paper investigates how to properly join precoded closed-loop MIMO systems and nonbinary low density parity check (NB-LDPC. The q elements in the Galois field, GF(q, are directly mapped to q transmit symbol vectors. This allows NB-LDPC codes to perfectly fit with a MIMO precoding scheme, unlike binary LDPC codes. The new transmission model is detailed and studied for several linear precoders and various designed LDPC codes. We show that NB-LDPC codes are particularly well suited to be jointly used with precoding schemes based on the maximization of the minimum Euclidean distance (max-dmin criterion. These results are theoretically supported by extrinsic information transfer (EXIT analysis and are confirmed by numerical simulations.

  15. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  16. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus

    2006-01-01

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...

  17. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...

  18. Use of the multinomial jack-knife and bootstrap in generalized non-linear canonical correlation analysis

    NARCIS (Netherlands)

    Burg, van der Eeke; Leeuw, de Jan

    1988-01-01

    In this paper we discuss the estimation of mean and standard errors of the eigenvalues and category quantifications in generalized non-linear canonical correlation analysis (OVERALS). Starting points are the delta method equations, but the jack-knife and bootstrap are used to provide finite differen

  19. Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging.

    Science.gov (United States)

    Nunes-Alves, Ariane; Arantes, Guilherme Menegon

    2014-08-25

    Accurate calculations of free energies involved in small-molecule binding to a receptor are challenging. Interactions between ligand, receptor, and solvent molecules have to be described precisely, and a large number of conformational microstates has to be sampled, particularly for ligand binding to a flexible protein. Linear interaction energy models are computationally efficient methods that have found considerable success in the prediction of binding free energies. Here, we parametrize a linear interaction model for implicit solvation with coefficients adapted by ligand and binding site relative polarities in order to predict ligand binding free energies. Results obtained for a diverse series of ligands suggest that the model has good predictive power and transferability. We also apply implicit ligand theory and propose approximations to average contributions of multiple ligand-receptor poses built from a protein conformational ensemble and find that exponential averages require proper energy discrimination between plausible binding poses and false-positives (i.e., decoys). The linear interaction model and the averaging procedures presented can be applied independently of each other and of the method used to obtain the receptor structural representation.

  20. Adaptive vision-based control of an unmanned aerial vehicle without linear velocity measurements.

    Science.gov (United States)

    Jabbari Asl, Hamed; Yoon, Jungwon

    2016-11-01

    In this paper, an image-based visual servo controller is designed for an unmanned aerial vehicle. The main objective is to use flow of image features as the velocity cue to compensate for the low quality of linear velocity information obtained from accelerometers. Nonlinear observers are designed to estimate this flow. The proposed controller is bounded, which can help to keep the target points in the field of view of the camera. The main advantages over the previous full dynamic observer-based methods are that, the controller is robust with respect to unknown image depth, and also no yaw information is required. The complete stability analysis is presented and asymptotic convergence of the error signals is guaranteed. Simulation results show the effectiveness of the proposed approach.

  1. Adaptive Digital Predistortion with Iterative Noise Cancelation for Power Amplifier Linearization

    Science.gov (United States)

    Jeon, Sungho; Kim, Junghyun; Lee, Jaekwon; Suh, Young-Woo; Seo, Jong-Soo

    In this paper, we propose a power amplifier linearization technique combined with iterative noise cancelation. This method alleviates the effect of added noises which prevents the predistorter (PD) from estimating the exact characteristics of the power amplifier (PA). To iteratively cancel the noise added in the feedback signal, the output signal of the power amplifier without noise is reconstructed by applying the inverse characteristics of the PD to the predistorted signals. The noise can be revealed by subtracting the reconstructed signals from the feedback signals. Simulation results based on the mean-square error (MSE) and power spectral density (PSD) criteria are presented to evaluate PD performance. The results show that the iterative noise cancelation significantly enhances the MSE performance, which leads to an improvement of the out-of-band power suppression. The performance of the proposed technique is verified by computer simulation and hardware test results.

  2. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

    Science.gov (United States)

    Yoo, Yun Joo; Sun, Lei; Poirier, Julia G.; Paterson, Andrew D.

    2016-01-01

    ABSTRACT By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. PMID:27885705

  3. Adaptive change of basis in entropy-based moment closures for linear kinetic equations

    CERN Document Server

    Alldredge, Graham W; O'Leary, Dianne P; Tits, André L

    2013-01-01

    Entropy-based (M_N) moment closures for kinetic equations are defined by a constrained optimization problem that must be solved at every point in a space-time mesh, making it important to solve these optimization problems accurately and efficiently. We present a complete and practical numerical algorithm for solving the dual problem in one-dimensional, slab geometries. The closure is only well-defined on the set of moments that are realizable from a positive underlying distribution, and as the boundary of the realizable set is approached, the dual problem becomes increasingly difficult to solve due to ill-conditioning of the Hessian matrix. To improve the condition number of the Hessian, we advocate the use of a change of polynomial basis, defined using a Cholesky factorization of the Hessian, that permits solution of problems nearer to the boundary of the realizable set. We also advocate a fixed quadrature scheme, rather than adaptive quadrature, since the latter introduces unnecessary expense and changes th...

  4. Linear systems modeling of adaptive optics in the spatial-frequency domain.

    Science.gov (United States)

    Ellerbroek, Brent L

    2005-02-01

    Spatial-frequency domain techniques have traditionally been applied to obtain estimates for the independent effects of a variety of individual error sources in adaptive optics (AO). Overall system performance is sometimes estimated by introducing the approximation that these individual error terms are statistically independent, so that their magnitudes may be summed in quadrature. More accurate evaluation methods that account for the correlations between the individual error sources have required Monte Carlo simulations or large matrix calculations that can take much longer to compute, particularly as the order of the AO system increases beyond a few hundred degrees of freedom. We describe an approach to evaluating AO system performance in the spatial-frequency domain that is relatively computationally efficient but still accounts for many of the interactions between the fundamental error sources in AO. We exploit the fact that (in the limits of an infinite aperture and geometrical optics) all the basic wave-front propagation, sensing, and correction processes that describe the behavior of an AO system are spatial-filtering operations in the Fourier domain. Essentially all classical wave-front control algorithms and evaluation formulas are expressed in terms of these filters and may therefore be evaluated one spatial-frequency component at a time. Performance estimates for very-high-order AO systems may be obtained in 1 to 2 orders of magnitude less time than needed when detailed simulations or analytical models in the spatial domain are used, with a relative discrepancy of 5% to 10% for typical sample problems.

  5. Neuro-Self Tuning Adaptive Controller for Non-Linear Dynamical Systems

    Directory of Open Access Journals (Sweden)

    Ahmed Sabah Abdul Ameer Al-Araji

    2005-01-01

    Full Text Available In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is applied to learn the control structure for self-tuning PID type neuro-controller. Where the neural network is used to minimize the error function by adjusting the PID gains. Simulation results show that the self-tuning PID scheme can deal with a large unknown nonlinearity.

  6. A generalized hybrid transfinite element computational approach for nonlinear/linear unified thermal/structural analysis

    Science.gov (United States)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1987-01-01

    The present paper describes the development of a new hybrid computational approach for applicability for nonlinear/linear thermal structural analysis. The proposed transfinite element approach is a hybrid scheme as it combines the modeling versatility of contemporary finite elements in conjunction with transform methods and the classical Bubnov-Galerkin schemes. Applicability of the proposed formulations for nonlinear analysis is also developed. Several test cases are presented to include nonlinear/linear unified thermal-stress and thermal-stress wave propagations. Comparative results validate the fundamental capablities of the proposed hybrid transfinite element methodology.

  7. Generalized Monge-Kantorovich optimization for grid generation and adaptation in LP

    Energy Technology Data Exchange (ETDEWEB)

    Delzanno, G L [Los Alamos National Laboratory; Finn, J M [Los Alamos National Laboratory

    2009-01-01

    The Monge-Kantorovich grid generation and adaptation scheme of is generalized from a variational principle based on L{sub 2} to a variational principle based on L{sub p}. A generalized Monge-Ampere (MA) equation is derived and its properties are discussed. Results for p > 1 are obtained and compared in terms of the quality of the resulting grid. We conclude that for the grid generation application, the formulation based on L{sub p} for p close to unity leads to serious problems associated with the boundary. Results for 1.5 {approx}< p {approx}< 2.5 are quite good, but there is a fairly narrow range around p = 2 where the results are close to optimal with respect to grid distortion. Furthermore, the Newton-Krylov methods used to solve the generalized MA equation perform best for p = 2.

  8. Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects.

    Directory of Open Access Journals (Sweden)

    Nengjun Yi

    2011-12-01

    Full Text Available Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/.

  9. Hierarchical Generalized Linear Models for Multiple Groups of Rare and Common Variants: Jointly Estimating Group and Individual-Variant Effects

    Science.gov (United States)

    Yi, Nengjun; Liu, Nianjun; Zhi, Degui; Li, Jun

    2011-01-01

    Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). PMID:22144906

  10. MRSA model of learning and adaptation: a qualitative study among the general public

    Directory of Open Access Journals (Sweden)

    Rohde Rodney E

    2012-04-01

    Full Text Available Abstract Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1 a common model of MRSA learning and adaptation; 2 the self-directed nature of adult learning; 3 the focus on general MRSA information, care and

  11. Linearized Alternating Direction Method with Adaptive Penalty and Warm Starts for Fast Solving Transform Invariant Low-Rank Textures

    CERN Document Server

    Ren, Xiang

    2012-01-01

    Transform Invariant Low-rank Textures (TILT) is a novel and powerful tool that can effectively rectify a rich class of low-rank textures in 3D scenes from 2D images despite significant deformation and corruption. The existing algorithm for solving TILT is based on the alternating direction method (ADM). It suffers from high computational cost and is not theoretically guaranteed to converge to a correct solution. In this paper, we propose a novel algorithm to speed up solving TILT, with guaranteed convergence. Our method is based on the recently proposed linearized alternating direction method with adaptive penalty (LADMAP). To further reduce computation, warm starts are also introduced to initialize the variables better and cut the cost on singular value decomposition. Extensive experimental results on both synthetic and real data demonstrate that this new algorithm works much more efficiently and robustly than the existing algorithm. It could be at least five times faster than the previous method.

  12. Adapting generalization tools to physiographic diversity for the united states national hydrography dataset

    Science.gov (United States)

    Buttenfield, B.P.; Stanislawski, L.V.; Brewer, C.A.

    2011-01-01

    This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate (dry or humid) can support decisions about algorithms, parameters, and processing sequences to create generalized, smaller scale data versions which preserve distinct hydrographic patterns in these regions. We work with multiple subbasins of the NHD that provide a range of terrain and climate characteristics. Specifically tailored generalization sequences are used to create simplified versions of the high resolution data, which was compiled for 1:24,000 scale mapping. Results are evaluated cartographically and metrically against a medium resolution benchmark version compiled for 1:100,000, developing coefficients of linear and areal correspondence.

  13. Effects of generalized and specialized adaptive defense by shared prey on intra-guild predation.

    Science.gov (United States)

    Ikegawa, Yusuke; Ezoe, Hideo; Namba, Toshiyuki

    2015-01-07

    Intra-guild predation (IGP), predation on consumers which share common prey with the predators, is an important community module to understand a mechanism for persistence of complex food webs. However, classical theory suggests that persistence of an IGP system is unlikely particularly at high productivity, while empirical data do not support the prediction. Recently, adaptive defense by shared prey has been recognized to enhance coexistence of species and stability of the system. Some organisms having multiple predators in IGP systems employ two types of defenses; generalized defense that is effective against multiple predators and specialized one that is effective against only a specific predator species. We consider an IGP model including shared prey that can use the two types of defenses in combination against the consumer or omnivore. Assuming that the shared prey can change the allocation of defensive effort to increase its fitness, we show that the joint use of two types of adaptive defenses promotes three species coexistence and enhances stability of the IGP system when the specialized defense is more effective than the generalized one. When the system is unstable, a variety of oscillations appear and both the population densities and defensive efforts or only the population densities oscillate. Joint use of defenses against the consumer tends to increase the equilibrium population density of the shared prey with the defense efficiencies. In contrast, efficient generalized and specialized defenses against the omnivore often decrease the prey population. Consequently, adaptive defense by shared prey may not necessarily heighten the population size of the defender but sometimes increases densities of both the attackers and defender in IGP systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Some Numerical Methods for Exponential Analysis with Connection to a General Identification Scheme for Linear Processes

    Science.gov (United States)

    1980-11-01

    generalized nodel described by Eykhoff [1, 2], Astrom and Eykhoff [3], and on pages 209-220 of Eykhoff [4]. The origin of the general- ized model can be...aspects of process-parameter estimation," IEEE Trans. Auto. Control, October 1963, pp. 347-357. 3. K. J. Astrom and P. Eykhoff, "System

  15. A general derivation of the subharmonic threshold for non-linear bubble oscillations

    NARCIS (Netherlands)

    Prosperetti, A.

    2013-01-01

    The paper describes an approximate but rather general derivation of the acoustic threshold for a subharmonic component to be possible in the sound scattered by an insonified gas bubble. The general result is illustrated with several specific models for the mechanical behavior of the surface coating

  16. Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models

    DEFF Research Database (Denmark)

    Lanne, Markku; Nyberg, Henri

    We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use...

  17. Continuity and general perturbation of the Drazin inverse for closed linear operators

    Directory of Open Access Journals (Sweden)

    N. Castro González

    2002-01-01

    Full Text Available We study perturbations and continuity of the Drazin inverse of a closed linear operator A and obtain explicit error estimates in terms of the gap between closed operators and the gap between ranges and nullspaces of operators. The results are used to derive a theorem on the continuity of the Drazin inverse for closed operators and to describe the asymptotic behavior of operator semigroups.

  18. Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT.

    Science.gov (United States)

    Borgen, Lars; Kalra, Mannudeep K; Laerum, Frode; Hachette, Isabelle W; Fredriksson, Carina H; Sandborg, Michael; Smedby, Orjan

    2012-04-01

    Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI (vol)) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m(2), 3D filtered images are comparable to standard dose images.

  19. General up regulation of Spodoptera frugiperda trypsins and chymotrypsins allows its adaptation to soybean proteinase inhibitor.

    Science.gov (United States)

    Brioschi, Daniela; Nadalini, Larissa D; Bengtson, Mario H; Sogayar, Mari Cleide; Moura, Daniel S; Silva-Filho, Marcio C

    2007-12-01

    The existence of a diverse serine proteinase gene family in lepidopteran insects suggests they play a significant role in the insect adaptation to plant proteinase inhibitors. These proteinases have been shown to be involved in the process of proteolytic digestion in insect larvae. We carried out a selective transcriptome study of midguts from Spodoptera frugiperda larvae fed on a diet supplemented with soybean proteinase inhibitor (SPI). Using subtracted cDNA libraries made of gut-expressed transcripts, a total of 2100 partial sequences were obtained, of those 38% were related to digestive process. Two large and diverse groups of chymotrypsins and trypsins were obtained, and some of these proteinase-encoding genes were further characterized by quantitative RT-PCR. The transcription analyses revealed two groups: one group of genes constitutively expressed in the control larvae that is up regulated by introducing SPI to the diet, and a second group that is absent in the control but is induced by the SPI-rich diet. This observation suggests that adaptation of S. frugiperda to SPI involves de novo synthesis and also up regulation of existing enzymes. Proteases from intestines of larvae reared on a diet with SPI showed insensitivity to the inhibitor. The proteases were also insensitive to a broad-spectrum potato proteinase inhibitor preparation. We propose that adaptation of S. frugiperda to SPI follows a "shotgun" approach, based on a general up regulation of a large set of endoproteinases.

  20. Adaptive finite element modeling of direct current resistivity in 2-D generally anisotropic structures

    Science.gov (United States)

    Yan, Bo; Li, Yuguo; Liu, Ying

    2016-07-01

    In this paper, we present an adaptive finite element (FE) algorithm for direct current (DC) resistivity modeling in 2-D generally anisotropic conductivity structures. Our algorithm is implemented on an unstructured triangular mesh that readily accommodates complex structures such as topography and dipping layers and so on. We implement a self-adaptive, goal-oriented grid refinement algorithm in which the finite element analysis is performed on a sequence of refined grids. The grid refinement process is guided by an a posteriori error estimator. The problem is formulated in terms of total potentials where mixed boundary conditions are incorporated. This type of boundary condition is superior to the Dirichlet type of conditions and improves numerical accuracy considerably according to model calculations. We have verified the adaptive finite element algorithm using a two-layered earth with azimuthal anisotropy. The FE algorithm with incorporation of mixed boundary conditions achieves high accuracy. The relative error between the numerical and analytical solutions is less than 1% except in the vicinity of the current source location, where the relative error is up to 2.4%. A 2-D anisotropic model is used to demonstrate the effects of anisotropy upon the apparent resistivity in DC soundings.

  1. Major depressive disorder in the general hospital: adaptation of clinical practice guidelines.

    Science.gov (United States)

    Voellinger, Rachel; Berney, Alexandre; Baumann, Pierre; Annoni, Jean Marie; Bryois, Christian; Buclin, Thierry; Büla, Christophe; Camus, Vincent; Christin, Laurent; Cornuz, Jacques; de Goumoëns, Pierre; Lamy, Olivier; Strnad, Jindrich; Burnand, Bernard; Stiefel, Frederic

    2003-01-01

    Major Depressive Disorder is particularly frequent among physically ill inpatients. Despite the considerable human burden and financial costs, Major Depressive Disorder remains under-detected and under-treated. To improve this situation, clinical practice guidelines for the management of Major Depressive Disorder were developed for patients in the general hospital. They were adapted from existing good quality guidelines. A literature search has been conducted to identify guidelines and systematic reviews about the management of Major Depressive Disorder. The quality of the existing guidelines was evaluated by means of the AGREE instrument (Appraisal of Guidelines for Research and Evaluation). Complementary literature searches were necessary to answer questions such as "depression and physical illness" or "antidepressants and somatic medication". The guidelines were discussed by a multidisciplinary internal panel. The final version was reviewed by an external panel. This paper presents the development process and a summary of these guidelines for the management of Major Depressive Disorder. The adaptation of good quality guidelines to local needs requires much time, effort and skills. Easier ways for the adaptation and use of high quality guidelines at the local level may result from better coordination, organization and updating of guidelines at a national or supranational level.

  2. Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

    Directory of Open Access Journals (Sweden)

    Matthew P. Dannenberg

    2016-08-01

    Full Text Available Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time.

  3. LINEAR STIELTJES EQUATION WITH GENERALIZED RIEMANN INTEGRAL AND EXISTENCE OF REGULATED SOLUTIONS

    Institute of Scientific and Technical Information of China (English)

    L. BARBANTI

    2001-01-01

    In this work we establish an existence theorem of regulated solutions for a class of Stieltjes equations which involve generalized Riemann kind of integrals. The general method applied consists in considering the continuous-time Stieltjes equation as limit of discrete processes. This approach will prove fruitful in the study of the controllability of Stieltjes systems, because it will be possible to get properties on the continuous time equation by transferring properties of the discrete ones.

  4. Binary System with Components of Different Masses in the Linear Regime of the Characteristic Formulation of General Relativity

    CERN Document Server

    M., C E Cedeño

    2015-01-01

    A study of binary systems composed of two point particles with different masses in the linear regime of the characteristic formulation of general relativity is provided. The boundary conditions at the world tubes generated by the particle's orbits are explored, when the metric variables are decomposed in spin-weighted spherical harmonics. The power lost by the emission of gravitational waves is computed using the News Bondi's functions, and the contribution to the gravitational radiation of several multipole terms is shown.

  5. LINEAR GENERAL EQUILIBRIUM MODEL OF ENERGY DEMAND AND CO2 EMISSIONS GENERATED BY THE ANDALUSIAN PRODUCTIVE SYSTEM

    Directory of Open Access Journals (Sweden)

    Manuel Alejandro Cardenete

    2012-01-01

    Full Text Available In this study we apply a multiplier decomposition methodology of a linear general equilibrium model based on the regional social accounting matrix to the Andalusian economy. The aim of this methodology is to separate the size of the different effects in terms of energy expenditure and total emissions generated by the whole productive system to satisfy the final demand of each branch of the Andalusian economy and the direct emissions generated to produce energy for each subsystem.

  6. CABARET scheme for the numerical solution of aeroacoustics problems: Generalization to linearized one-dimensional Euler equations

    Science.gov (United States)

    Goloviznin, V. M.; Karabasov, S. A.; Kozubskaya, T. K.; Maksimov, N. V.

    2009-12-01

    A generalization of the CABARET finite difference scheme is proposed for linearized one-dimensional Euler equations based on the characteristic decomposition into local Riemann invariants. The new method is compared with several central finite difference schemes that are widely used in computational aeroacoustics. Numerical results for the propagation of an acoustic wave in a homogeneous field and the refraction of this wave through a contact discontinuity obtained on a strongly nonuniform grid are presented.

  7. AN ACCURATE SOLUTION OF THE LINEAR THEORY OF THE WIND-DRIVEN OCEAN CIRCULATION-I. THE GENERALIZED SOLUTION

    Institute of Scientific and Technical Information of China (English)

    Zhang Qing-hua; Qu Yuan-yuan; Xia Chang-shui

    2003-01-01

    To model the wind-driven ocean circulation of the isobath rectangular basin, the linear vorticity equation with the meridional friction term was used compared to the Munk's theory on the ocean circulation. The generalized solution of the vorticity equation was thus worked out in the sense of Fourier averaging by using the corrected Fourier expansion. The method to obtain the undetermined coefficients was presented using the viscous boundary conditions.

  8. GENERAL CAUCHY PROBLEM FOR THE LINEAR SHALLOW -WATER EQUATIONS ON AN EQUATORIAL BETA-PLANE

    Institute of Scientific and Technical Information of China (English)

    SHEN Chun; SHI Wei-hui

    2006-01-01

    Based on the theory of stratification, the well-posedness of the initial value problem for the linear shallow-water equations on an equatorial beta-plane was discussed. The sufficient and necessary conditions of the existence and uniqueness for the local solution of the equations were presented and the existence conditions for formal solutions of the equations were also given. For the Cauchy problem on the hyper-plane, the local analytic solution were worked out and a special case was discussed. Finally, an example was used to explain the variety of formal solutions for the ill-posed problem.

  9. Generalized Coherent States of a Particle in a Time-Dependent Linear Potential

    Institute of Scientific and Technical Information of China (English)

    L.Krache; M.Maamache; Y.Saadi; A.Beniaiche

    2009-01-01

    We derive, with an invariant operator method and unitary transformation approach, that the Schr(o)dinger equation with a time-dependent linear potential possesses an infinite string of shape-preseving wave-packet states |ψα,λ(t)>having classical motion. The qualitative properties of the invariant eigenvalue spectrum (discrete or continuous)are described separately for the different values of the frequency ω of a harmonic oscillator. It is also shown that,for a discrete eigenvalue spectrum, the states |ψα,n(t)> could be obtained from the coherent state |ψα,0(t)>.

  10. Examining secular trend  and seasonality in count data using dynamic generalized linear modelling

    DEFF Research Database (Denmark)

    Lundbye-Christensen, Søren; Dethlefsen, Claus; Gorst-Rasmussen, Anders;

    series regression model for Poisson counts. It differs in allowing the regression coefficients to vary gradually over time in a random fashion. Data  In the period January 1980 to 1999, 17,989 incidents of acute myocardial infarction were recorded in the county of Northern Jutland, Denmark. Records were...... updated daily. Results  The model with a seasonal pattern and an approximately linear trend was fitted to the data, and diagnostic plots indicate a good model fit. The analysis with the dynamic model revealed peaks coinciding with influenza epidemics. On average the peak-to-trough ratio is estimated...

  11. Identification of general linear relationships between activation energies and enthalpy changes for dissociation reactions at surfaces.

    Science.gov (United States)

    Michaelides, Angelos; Liu, Z-P; Zhang, C J; Alavi, Ali; King, David A; Hu, P

    2003-04-02

    The activation energy to reaction is a key quantity that controls catalytic activity. Having used ab inito calculations to determine an extensive and broad ranging set of activation energies and enthalpy changes for surface-catalyzed reactions, we show that linear relationships exist between dissociation activation energies and enthalpy changes. Known in the literature as empirical Brønsted-Evans-Polanyi (BEP) relationships, we identify and discuss the physical origin of their presence in heterogeneous catalysis. The key implication is that merely from knowledge of adsorption energies the barriers to catalytic elementary reaction steps can be estimated.

  12. Sparse non-linear denoising: Generalization performance and pattern reproducibility in functional MRI

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    We investigate sparse non-linear denoising of functional brain images by kernel Principal Component Analysis (kernel PCA). The main challenge is the mapping of denoised feature space points back into input space, also referred to as ”the pre-image problem”. Since the feature space mapping...... sparse pre-image reconstruction by Lasso regularization. We find that sparse estimation provides better brain state decoding accuracy and a more reproducible pre-image. These two important metrics are combined in an evaluation framework which allow us to optimize both the degree of sparsity and the non...

  13. General theory of spherically symmetric boundary-value problems of the linear transport theory.

    Science.gov (United States)

    Kanal, M.

    1972-01-01

    A general theory of spherically symmetric boundary-value problems of the one-speed neutron transport theory is presented. The formulation is also applicable to the 'gray' problems of radiative transfer. The Green's function for the purely absorbing medium is utilized in obtaining the normal mode expansion of the angular densities for both interior and exterior problems. As the integral equations for unknown coefficients are regular, a general class of reduction operators is introduced to reduce such regular integral equations to singular ones with a Cauchy-type kernel. Such operators then permit one to solve the singular integral equations by the standard techniques due to Muskhelishvili. We discuss several spherically symmetric problems. However, the treatment is kept sufficiently general to deal with problems lacking azimuthal symmetry. In particular the procedure seems to work for regions whose boundary coincides with one of the coordinate surfaces for which the Helmholtz equation is separable.

  14. Extending generalized linear models with random effects and components of dispersion.

    NARCIS (Netherlands)

    Engel, B.

    1997-01-01

    This dissertation was born out of a need for general and numerically feasible procedures for inference in variance components models for non-normal data. The methodology should be widely applicable within the institutes of the Agricultural Research Department (DLO) of the Dutch Ministry of Agricultu

  15. Size-extensive wave functions for quantum Monte Carlo: A linear scaling generalized valence bond approach

    NARCIS (Netherlands)

    Fracchia, F.; Filippi, C.; Amovilli, C.

    2012-01-01

    We propose a new class of multideterminantal Jastrow–Slater wave functions constructed with localized orbitals and designed to describe complex potential energy surfaces of molecular systems for use in quantum Monte Carlo (QMC). Inspired by the generalized valence bond formalism, we elaborate a coup

  16. General polynomial factorization-based design of sparse periodic linear arrays.

    Science.gov (United States)

    Mitra, Sanjit K; Mondal, Kalyan; Tchobanou, Mikhail K; Dolecek, Gordana Jovanovic

    2010-09-01

    We have developed several methods of designing sparse periodic arrays based upon the polynomial factorization method. In these methods, transmit and receive aperture polynomials are selected such that their product results in a polynomial representing the desired combined transmit/receive (T/R) effective aperture function. A desired combined T/R effective aperture is simply an aperture with an appropriate width exhibiting a spectrum that corresponds to the desired two-way radiation pattern. At least one of the two aperture functions that constitute the combined T/R effective aperture function will be a sparse polynomial. A measure of sparsity of the designed array is defined in terms of the element reduction factor. We show that elements of a linear array can be reduced with varying degrees of beam mainlobe width to sidelobe reduction properties.

  17. Linear stability of plane Poiseuille flow over a generalized Stokes layer

    Energy Technology Data Exchange (ETDEWEB)

    Quadrio, Maurizio [Dip. Ing. Aerospaziale, Politecnico di Milano, Campus Bovisa, I-20156 Milano (Italy); Martinelli, Fulvio; Schmid, Peter J, E-mail: maurizio.quadrio@polimi.it [Laboratoire d' Hydrodynamique (LadHyX), CNRS-Ecole Polytechnique, F-91128 Palaiseau (France)

    2011-12-22

    Linear stability of plane Poiseuille flow subject to spanwise velocity forcing applied at the wall is studied. The forcing is stationary and sinusoidally distributed along the streamwise direction. The long-term aim of the study is to explore a possible relationship between the modification induced by the wall forcing to the stability characteristic of the unforced Poiseuille flow and the signifcant capabilities demonstrated by the same forcing in reducing turbulent friction drag. We present in this paper the statement of the mathematical problem, which is considerably more complex that the classic Orr-Sommerfeld-Squire approach, owing to the streamwise-varying boundary condition. We also report some preliminary results which, although not yet conclusive, describe the effects of the wall forcing on modal and non-modal characteristics of the flow stability.

  18. Iterative solution of general sparse linear systems on clusters of workstations

    Energy Technology Data Exchange (ETDEWEB)

    Lo, Gen-Ching; Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)

    1996-12-31

    Solving sparse irregularly structured linear systems on parallel platforms poses several challenges. First, sparsity makes it difficult to exploit data locality, whether in a distributed or shared memory environment. A second, perhaps more serious challenge, is to find efficient ways to precondition the system. Preconditioning techniques which have a large degree of parallelism, such as multicolor SSOR, often have a slower rate of convergence than their sequential counterparts. Finally, a number of other computational kernels such as inner products could ruin any gains gained from parallel speed-ups, and this is especially true on workstation clusters where start-up times may be high. In this paper we discuss these issues and report on our experience with PSPARSLIB, an on-going project for building a library of parallel iterative sparse matrix solvers.

  19. A Generalized Linear Transport Model for Spatially-Correlated Stochastic Media

    CERN Document Server

    Davis, Anthony B

    2014-01-01

    We formulate a new model for transport in stochastic media with long-range spatial correlations where exponential attenuation (controlling the propagation part of the transport) becomes power law. Direct transmission over optical distance $\\tau(s)$, for fixed physical distance $s$, thus becomes $(1+\\tau(s)/a)^{-a}$, with standard exponential decay recovered when $a\\to\\infty$. Atmospheric turbulence phenomenology for fluctuating optical properties rationalizes this switch. Foundational equations for this generalized transport model are stated in integral form for $d=1,2,3$ spatial dimensions. A deterministic numerical solution is developed in $d=1$ using Markov Chain formalism, verified with Monte Carlo, and used to investigate internal radiation fields. Standard two-stream theory, where diffusion is exact, is recovered when $a=\\infty$. Differential diffusion equations are not presently known when $a<\\infty$, nor is the integro-differential form of the generalized transport equation. Monte Carlo simulations...

  20. A Generalized Multiscale Finite Element Method for Poroelasticity Problems I: Linear Problems

    CERN Document Server

    Brown, Donald L

    2015-01-01

    In this paper, we consider the numerical solution of poroelasticity problems that are of Biot type and develop a general algorithm for solving coupled systems. We discuss the challenges associated with mechanics and flow problems in heterogeneous media. The two primary issues being the multiscale nature of the media and the solutions of the fluid and mechanics variables traditionally developed with separate grids and methods. For the numerical solution we develop and implement a Generalized Multiscale Finite Element Method (GMsFEM) that solves problem on a coarse grid by constructing local multiscale basis functions. The procedure begins with construction of multiscale bases for both displacement and pressure in each coarse block. Using a snapshot space and local spectral problems, we construct a basis of reduced dimension. Finally, after multiplying by a multiscale partitions of unity, the multiscale basis is constructed in the offline phase and the coarse grid problem then can be solved for arbitrary forcin...

  1. Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT

    Energy Technology Data Exchange (ETDEWEB)

    Borgen, Lars (Dept. of Radiology, Drammen Hospital, Drammen and Buskerud Univ. College, Drammen (Norway)), Email: lars.borgen@vestreviken.no; Kalra, Mannudeep K. (Massachusetts General Hospital Imaging, Harvard Medical School, Massachusetts General Hospital, Boston (United States)); Laerum, Frode (Dept. of Radiology, Akershus Univ. Hospital, Loerenskog (Norway)); Hachette, Isabelle W.; Fredriksson, Carina H. (ContextVision AB, Linkoeping (Sweden)); Sandborg, Michael (Dept. of Medical Physics, IMH, Faculty of Health Sciences, Linkoeping Univ., County Council of Oestergoetland, Linkoeping (Sweden); Center for Medical Image Science and Visualization, Linkoeping (Sweden)); Smedby, Oerjan (Center for Medical Image Science and Visualization, Linkoeping (Sweden); Dept. of Radiology, Linkoeping Univ., Linkoeping (Sweden))

    2012-04-15

    Background: Abdominal computed tomography (CT) is a frequently performed imaging procedure, resulting in considerable radiation doses to the patient population. Postprocessing filters are one of several dose reduction measures that might help to reduce radiation doses without loss of image quality. Purpose: To assess and compare the effect of two- and three-dimensional (2D, 3D) non-linear adaptive filters on reduced dose abdominal CT images. Material and Methods: Two baseline abdominal CT image series with a volume computer tomography dose index (CTDI{sub vol}) of 12 mGy and 6 mGy were acquired for 12 patients. Reduced dose images were postprocessed with 2D and 3D filters. Six radiologists performed blinded randomized, side-by-side image quality assessments. Objective noise was measured. Data were analyzed using visual grading regression and mixed linear models. Results: All image quality criteria were rated as superior for 3D filtered images compared to reduced dose baseline and 2D filtered images (P < 0.01). Standard dose images had better image quality than reduced dose 3D filtered images (P < 0.01), but similar image noise. For patients with body mass index (BMI) < 30 kg/m2 however, 3D filtered images were rated significantly better than normal dose images for two image criteria (P < 0.05), while no significant difference was found for the remaining three image criteria (P > 0.05). There were no significant variations of objective noise between standard dose and 2D or 3D filtered images. Conclusion: The quality of 3D filtered reduced dose abdominal CT images is superior compared to reduced dose unfiltered and 2D filtered images. For patients with BMI < 30 kg/m2, 3D filtered images are comparable to standard dose images

  2. Approximation of Conjugate Functions by General Linear Operators of Their Fourier Series at the Lebesgue Points

    Directory of Open Access Journals (Sweden)

    Łenski Włodzimierz

    2014-12-01

    Full Text Available The pointwise estimates of the deviations r T͂n,A,Bf (· - f͂͂ (· and T͂n,A,Bf (· - f͂͂ (·,ε in terms of moduli of continuity ω̃f and r ω̃f are proved. Analogical results on norm approximation with remarks and corollary are also given. These results generalized a theorem of Mittal [3, Theorem 1, p. 437].

  3. A generalized Lyapunov theory for robust root clustering of linear state space models with real parameter uncertainty

    Science.gov (United States)

    Yedavalli, R. K.

    1992-01-01

    The problem of analyzing and designing controllers for linear systems subject to real parameter uncertainty is considered. An elegant, unified theory for robust eigenvalue placement is presented for a class of D-regions defined by algebraic inequalities by extending the nominal matrix root clustering theory of Gutman and Jury (1981) to linear uncertain time systems. The author presents explicit conditions for matrix root clustering for different D-regions and establishes the relationship between the eigenvalue migration range and the parameter range. The bounds are all obtained by one-shot computation in the matrix domain and do not need any frequency sweeping or parameter gridding. The method uses the generalized Lyapunov theory for getting the bounds.

  4. A Generalization of Palmer's Linearization Theorem%Palmer线性化定理的一个推广

    Institute of Scientific and Technical Information of China (English)

    江良平

    2011-01-01

    本文在对二分性函数a(t)有适当的要求的条件下将Palmer线性化定理推广到线性部分只具有广义指数型二分性的系统,并在一定条件下证明了等价函数的强一致连续性.%In this paper, under the condition that dichotomy function a(t) satisfies some certain condition,the author extend Palmer's linearization theorem to the systems which linear parts admit generalized exponential dichotomy, meanwhile, the author prove that equivalent functions are strongly uniformly continuous.

  5. A generalized Lyapunov theory for robust root clustering of linear state space models with real parameter uncertainty

    Science.gov (United States)

    Yedavalli, R. K.

    1992-01-01

    The problem of analyzing and designing controllers for linear systems subject to real parameter uncertainty is considered. An elegant, unified theory for robust eigenvalue placement is presented for a class of D-regions defined by algebraic inequalities by extending the nominal matrix root clustering theory of Gutman and Jury (1981) to linear uncertain time systems. The author presents explicit conditions for matrix root clustering for different D-regions and establishes the relationship between the eigenvalue migration range and the parameter range. The bounds are all obtained by one-shot computation in the matrix domain and do not need any frequency sweeping or parameter gridding. The method uses the generalized Lyapunov theory for getting the bounds.

  6. Strain Mediated Adaptation Is Key for Myosin Mechanochemistry: Discovering General Rules for Motor Activity.

    Science.gov (United States)

    Jana, Biman; Onuchic, José N

    2016-08-01

    A structure-based model of myosin motor is built in the same spirit of our early work for kinesin-1 and Ncd towards physical understanding of its mechanochemical cycle. We find a structural adaptation of the motor head domain in post-powerstroke state that signals faster ADP release from it compared to the same from the motor head in the pre-powerstroke state. For dimeric myosin, an additional forward strain on the trailing head, originating from the postponed powerstroke state of the leading head in the waiting state of myosin, further increases the rate of ADP release. This coordination between the two heads is the essence of the processivity of the cycle. Our model provides a structural description of the powerstroke step of the cycle as an allosteric transition of the converter domain in response to the Pi release. Additionally, the variation in structural elements peripheral to catalytic motor domain is the deciding factor behind diverse directionalities of myosin motors (myosin V & VI). Finally, we observe that there are general rules for functional molecular motors across the different families. Allosteric structural adaptation of the catalytic motor head in different nucleotide states is crucial for mechanochemistry. Strain-mediated coordination between motor heads is essential for processivity and the variation of peripheral structural elements is essential for their diverse functionalities.

  7. A general hybrid radiation transport scheme for star formation simulations on an adaptive grid

    CERN Document Server

    Klassen, Mikhail; Pudritz, Ralph E; Peters, Thomas; Banerjee, Robi; Buntemeyer, Lars

    2014-01-01

    Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodynamics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion (FLD) solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calc...

  8. Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Dongyul Lee

    2014-01-01

    Full Text Available The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC with adaptive modulation and coding (AMC provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.

  9. Integer-linear-programing optimization in scalable video multicast with adaptive modulation and coding in wireless networks.

    Science.gov (United States)

    Lee, Dongyul; Lee, Chaewoo

    2014-01-01

    The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.

  10. Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

    Science.gov (United States)

    Lee, Chaewoo

    2014-01-01

    The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm. PMID:25276862

  11. Combining support vector machines with linear quadratic regulator adaptation for the online design of an automotive active suspension system

    Science.gov (United States)

    Chiou, J.-S.; Liu, M.-T.

    2008-02-01

    As a powerful machine-learning approach to pattern recognition problems, the support vector machine (SVM) is known to easily allow generalization. More importantly, it works very well in a high-dimensional feature space. This paper presents a nonlinear active suspension controller which achieves a high level performance by compensating for actuator dynamics. We use a linear quadratic regulator (LQR) to ensure optimal control of nonlinear systems. An LQR is used to solve the problem of state feedback and an SVM is used to address the question of the estimation and examination of the state. These two are then combined and designed in a way that outputs feedback control. The real-time simulation demonstrates that an active suspension using the combined SVM-LQR controller provides passengers with a much more comfortable ride and better road handling.

  12. Cross-Cultural adaptation of the General Functioning Scale of the Family

    Directory of Open Access Journals (Sweden)

    Thiago Pires

    2016-01-01

    Full Text Available ABSTRACT OBJECTIVE To describe the process of cross-cultural adaptation of the General Functioning Scale of the Family, a subscale of the McMaster Family Assessment Device, for the Brazilian population. METHODS The General Functioning Scale of the Family was translated into Portuguese and administered to 500 guardians of children in the second grade of elementary school in public schools of Sao Gonçalo, Rio de Janeiro, Southeastern Brazil. The types of equivalences investigated were: conceptual and of items, semantic, operational, and measurement. The study involved discussions with experts, translations and back-translations of the instrument, and psychometric assessment. Reliability and validity studies were carried out by internal consistency testing (Cronbach’s alpha, Guttman split-half correlation model, Pearson correlation coefficient, and confirmatory factor analysis. Associations between General Functioning of the Family and variables theoretically associated with the theme (father’s or mother’s drunkenness and violence between parents were estimated by odds ratio. RESULTS Semantic equivalence was between 90.0% and 100%. Cronbach’s alpha ranged from 0.79 to 0.81, indicating good internal consistency of the instrument. Pearson correlation coefficient ranged between 0.303 and 0.549. Statistical association was found between the general functioning of the family score and the theoretically related variables, as well as good fit quality of the confirmatory analysis model. CONCLUSIONS The results indicate the feasibility of administering the instrument to the Brazilian population, as it is easy to understand and a good measurement of the construct of interest.

  13. FPGA/NIOS Implementation of an Adaptive FIR Filter Using Linear Prediction to Reduce Narrow-Band RFI for Radio Detection of Cosmic Rays

    NARCIS (Netherlands)

    Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.

    2013-01-01

    We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by

  14. FPGA/NIOS Implementation of an Adaptive FIR Filter Using Linear Prediction to Reduce Narrow-Band RFI for Radio Detection of Cosmic Rays

    NARCIS (Netherlands)

    Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.

    2013-01-01

    We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by ultra-high-ene

  15. Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012: a systematic review.

    Directory of Open Access Journals (Sweden)

    Martí Casals

    Full Text Available BACKGROUND: Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. METHODS: A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. RESULTS: A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64 or Poisson (n = 22. Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%. The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. CONCLUSIONS: During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the

  16. A Priori Error Estimates of Mixed Finite Element Methods for General Linear Hyperbolic Convex Optimal Control Problems

    Directory of Open Access Journals (Sweden)

    Zuliang Lu

    2014-01-01

    Full Text Available The aim of this work is to investigate the discretization of general linear hyperbolic convex optimal control problems by using the mixed finite element methods. The state and costate are approximated by the k order (k≥0 Raviart-Thomas mixed finite elements and the control is approximated by piecewise polynomials of order k. By applying the elliptic projection operators and Gronwall’s lemma, we derive a priori error estimates of optimal order for both the coupled state and the control approximation.

  17. Fast adaptive principal component extraction based on a generalized energy function

    Institute of Scientific and Technical Information of China (English)

    欧阳缮; 保铮; 廖桂生

    2003-01-01

    By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari-ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation resultsshow that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance tothe eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction(LMSER) algorithms.

  18. Parameter estimation of linear and quadratic chirps by employing the fractional fourier transform and a generalized time frequency transform

    Indian Academy of Sciences (India)

    Shishir B Sahay; T Meghasyam; Rahul K Roy; Gaurav Pooniwala; Sasank Chilamkurthy; Vikram Gadre

    2015-06-01

    This paper is targeted towards a general readership in signal processing. It intends to provide a brief tutorial exposure to the Fractional Fourier Transform, followed by a report on experiments performed by the authors on a Generalized Time Frequency Transform (GTFT) proposed by them in an earlier paper. The paper also discusses the extension of the uncertainty principle to the GTFT. This paper discusses some analytical results of the GTFT. We identify the eigenfunctions and eigenvalues of the GTFT. The time shift property of the GTFT is discussed. The paper describes methods for estimation of parameters of individual chirp signals on receipt of a noisy mixture of chirps. A priori knowledge of the nature of chirp signals in the mixture – linear or quadratic is required, as the two proposed methods fall in the category of model-dependent methods for chirp parameter estimation.

  19. Point particle binary system with components of different masses in the linear regime of the characteristic formulation of general relativity

    Science.gov (United States)

    Cedeño M, C. E.; de Araujo, J. C. N.

    2016-05-01

    A study of binary systems composed of two point particles with different masses in the linear regime of the characteristic formulation of general relativity with a Minkowski background is provided. The present paper generalizes a previous study by Bishop et al. The boundary conditions at the world tubes generated by the particles's orbits are explored, where the metric variables are decomposed in spin-weighted spherical harmonics. The power lost by the emission of gravitational waves is computed using the Bondi News function. The power found is the well-known result obtained by Peters and Mathews using a different approach. This agreement validates the approach considered here. Several multipole term contributions to the gravitational radiation field are also shown.

  20. An algorithm for the construction of substitution box for block ciphers based on projective general linear group

    Directory of Open Access Journals (Sweden)

    Anas Altaleb

    2017-03-01

    Full Text Available The aim of this work is to synthesize 8*8 substitution boxes (S-boxes for block ciphers. The confusion creating potential of an S-box depends on its construction technique. In the first step, we have applied the algebraic action of the projective general linear group PGL(2,GF(28 on Galois field GF(28. In step 2 we have used the permutations of the symmetric group S256 to construct new kind of S-boxes. To explain the proposed extension scheme, we have given an example and constructed one new S-box. The strength of the extended S-box is computed, and an insight is given to calculate the confusion-creating potency. To analyze the security of the S-box some popular algebraic and statistical attacks are performed as well. The proposed S-box has been analyzed by bit independent criterion, linear approximation probability test, non-linearity test, strict avalanche criterion, differential approximation probability test, and majority logic criterion. A comparison of the proposed S-box with existing S-boxes shows that the analyses of the extended S-box are comparatively better.