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Sample records for adaptive general linear

  1. Adaptive quasi-likelihood estimate in generalized linear models

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

    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.

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

    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. Foundations of linear and generalized linear models

    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,

  5. Generalized, Linear, and Mixed Models

    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

  6. Speaker Adaptation with Transformation Matrix Linear Interpolation

    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.

  7. Linear zonal atmospheric prediction for adaptive optics

    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.

  8. Homology stability for the general linear group

    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

  9. Generalized Cross-Gramian for Linear Systems

    Shaker, Hamid Reza

    2012-01-01

    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...

  10. Linear generalized synchronization of chaotic systems with uncertain parameters

    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.

  11. 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

    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.

  12. General linear dynamics - quantum, classical or hybrid

    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.

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

    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.

  14. Generalized Adaptive Artificial Neural Networks

    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.

  15. Multivariate Generalized Linear Mixed Models Using R

    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

  16. Using R In Generalized Linear Models

    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.

  17. ADAPTIVE GENERALIZED PREDICTIVE CONTROL OF SWITCHED SYSTEMS

    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.

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

    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.

  19. Generalized Quadratic Linearization of Machine Models

    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...

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

    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...

  1. Rapid, generalized adaptation to asynchronous audiovisual speech.

    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.

  2. Penalized maximum likelihood estimation for generalized linear point processes

    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...

  3. Abstract Acceleration of General Linear Loops

    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...

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

    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.

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

    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.

  6. Coded Adaptive Linear Precoded Discrete Multitone Over PLC Channel

    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...

  7. Discrete Time Optimal Adaptive Control for Linear Stochastic Systems

    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.

  8. Generalized Ultrametric Semilattices of Linear Signals

    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

  9. QUADRATIC INVARIANTS AND SYMPLECTIC STRUCTURE OF GENERAL LINEAR METHODS

    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.

  10. Adaptive feedback linearization applied to steering of ships

    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.

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

    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

  12. Flexible Satellite Attitude Control via Adaptive Fuzzy Linearization

    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.

  13. Robust Adaptive Control via Neural Linearization and Compensation

    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.

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

    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

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

    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.

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

    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.

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

    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

  18. Natural connections given by general linear and classical connections

    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.

  19. Testing Parametric versus Semiparametric Modelling in Generalized Linear Models

    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(

  20. Minimal solution of general dual fuzzy linear systems

    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.

  1. Linear generalized synchronization of continuous-time chaotic systems

    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.

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

    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.

  3. Penalized maximum likelihood estimation for generalized linear point processes

    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....

  4. Linearly and Quadratically Separable Classifiers Using Adaptive Approach

    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.

  5. Controllability of Linear Systems on Generalized Heisenberg Groups

    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.

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

    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.

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

    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.

  8. Adaptive Unified Biased Estimators of Parameters in Linear Model

    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.

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

    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...

  10. General Linear Models: An Integrated Approach to Statistics

    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.

  11. McDonald Generalized Linear Failure Rate Distribution

    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.

  12. General Linear Models: An Integrated Approach to Statistics

    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...

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

    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.

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

    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 ...

  15. Testing for one Generalized Linear Single Order Parameter

    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...

  16. An Adaptive Non-Linear Map and Its Application

    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.

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

    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.

  18. A Matrix Approach for General Higher Order Linear Recurrences

    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

  19. The linear model and hypothesis a general unifying theory

    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.

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

    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...

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

    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.

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

    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

    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. The General Linear Model as Structural Equation Modeling

    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…

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

    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.

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

    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.

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

    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.

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

    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.

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

    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

    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. A new heuristic algorithm for general integer linear programming problems

    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.

  12. Regularization Paths for Generalized Linear Models via Coordinate Descent

    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.

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

    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.

  14. Computation of Optimal Monotonicity Preserving General Linear Methods

    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.

  15. General expression for linear and nonlinear time series models

    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.

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

    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...

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

    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.

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

    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....

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

    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.

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

    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.

  1. Conditional likelihood inference in generalized linear mixed models.

    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...

  2. Credibility analysis of risk classes by generalized linear model

    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.

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

    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...

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

    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.

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

    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.

  6. Local Linear Embedding Algorithm with Adaptively Determining Neighborhood

    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

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

    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.

  8. Generalized PID observer design for descriptor linear systems.

    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.

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

    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...

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

    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.

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

    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.

  12. Confidence Intervals of Variance Functions in Generalized Linear Model

    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.

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

    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....

  14. Plasticity-rigidity cycles: A general adaptation mechanism

    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...

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

    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.

  16. Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems

    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.

  17. Generalized space and linear momentum operators in quantum mechanics

    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.

  18. Generalized Ghost Dark Energy with Non-Linear Interaction

    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...

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

    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.

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

    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.

  1. Linear spin-2 fields in most general backgrounds

    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. Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project

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

  3. Adaptive Linear Parameter Varying Control for Aeroservoelastic Suppression Project

    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...

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

    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.

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

    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.

  6. A new family of gauges in linearized general relativity

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

  13. Adaptive Non-linear Control of Hydraulic Actuator Systems

    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)....

  14. A New Family of Gauges in Linearized General Relativity

    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...

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

    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.

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

    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.

  17. Synchronization of general complex networks via adaptive control schemes

    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.

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

    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

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

    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

  20. Linear Perturbation Adaptive Control of Hydraulically Driven Manipulators

    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...

  1. Bayesian inference for generalized linear models for spiking neurons

    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.

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

    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.

  3. Multivariate statistical modelling based on generalized linear models

    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...

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

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

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

    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.

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

    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.

  12. Generalization in adaptation to stable and unstable dynamics.

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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…

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

    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.

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

    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.

  20. PYESSENCE: Generalized Coupled Quintessence Linear Perturbation Python Code

    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.

  1. A General Linear Method for Equating with Small Samples

    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…

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

    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.

  3. Generalized linear IgA dermatosis with palmar involvement

    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 ...

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

  9. Generalized linear IgA dermatosis with palmar involvement.

    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.

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

    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

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

    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.

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

    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.

  13. Decentralized adaptive generalized predictive control for structural vibration

    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.

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

    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

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

    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

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

    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

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

    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

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

    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.

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

    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.

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

    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.

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

    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.

  2. Rayleigh-type Surface Quasimodes in General Linear Elasticity

    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.

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

    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.

  4. Item Response Theory Using Hierarchical Generalized Linear Models

    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.

  5. The left invariant metric in the general linear group

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

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

    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.

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

    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.

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

    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.

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

    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.

  15. Adaptive Elastic Net for Generalized Methods of Moments.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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)

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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...... 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....

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

    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.

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

    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.

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

    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...

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

    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)

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

    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.

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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…

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

    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).

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

    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.

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

    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.

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

    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.

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

    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.

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

    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).

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

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

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

    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.

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

    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).

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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...

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

    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...

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

    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.

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

    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.

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

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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...

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

    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 ...

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

    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.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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.

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

    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 ...

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

    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

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

    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.

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

    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.

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

    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.

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

    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.

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

    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)

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

    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.

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

    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.

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

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

    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...

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

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

    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 (...

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

    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 →∞.

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

    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→∞.

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

    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.

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

    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.

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

    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...

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

    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.

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

    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.

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

    Dlugosz, Stephan; Mammen, Enno; Wilke, Ralf

    2017-01-01

    or contain missing values. Given the size of the data, a flexible semiparametric misclassification model would be good choice but their use in practise is scarce. To close this gap a semiparametric model for the probability of observing labour market transitions is estimated using a sample of 20 m...... 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......Large data sets that originate from administrative or operational activity are increasingly used for statistical analysis as they often contain very precise information and a large number of observations. But there is evidence that some variables can be subject to severe misclassification...

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

    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.

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

    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.

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

    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.

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

    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.

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

    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...

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

    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

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

    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

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

    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

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

    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.

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

    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.

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

    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.

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

    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

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

    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.

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

    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.

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

    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

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

    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…

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

    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...

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

    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.

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

    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.

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

    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.

    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. Motor adaptation and generalization of reaching movements using motor primitives based on spatial coordinates.

    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.

  5. General Acid Catalysis: A Flexible Experiment, Adaptable to Student Ability and Various Teaching Approaches.

    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)

  6. A NEW SELF-ADAPTIVE ITERATIVE METHOD FOR GENERAL MIXED QUASI VARIATIONAL INEQUALITIES

    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.

  7. An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

    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.

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

    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.

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

    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.

  10. c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models

    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.

  11. The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

    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.

  12. MGMRES: A generalization of GMRES for solving large sparse nonsymmetric linear systems

    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. Motor learning and general adaptation syndrome Aprendizaje motor y síndrome general de adaptación

    E. M. Ordoño

    2010-09-01

    Full Text Available

    This work examines the General Adaptation Syndrome like a suitable framework to explain motor learning processes. Human motor behaviour is viewed like a complex system continuously interacting in the environment. Motor learning is proposed as an adaptation process to the tasks constraints. Training loads and practice load are also considered analogous. Practice is the vehicle of learning, but it must be applied with the enough amount of load to produce an adaptation to a new level of performance. The principles of sport training are presented related to motor learning topics. Common principles are proposed to explain the learning of motor skills, regardless of the level of complexity, and level of the performer, and providing basic criteria that should help to design learning tasks.
    Key Words:  Motor learning, adaptation, complex systems, training, motor skills.

     

    Este trabajo examina las posibilidades del Síndrome General de Adaptación como un marco de referencia para explicar y predecir los cambios producidos por el Aprendizaje Motor. Se parte de la consideración del ser humano como un sistema complejo en continua interacción con su entorno y el aprendizaje como un proceso de adaptación a las condiciones impuestas por la tarea. Se propone el concepto de carga de práctica análogo al de carga de entrenamiento, considerando que la práctica, vehículo del aprendizaje, debe aplicarse como una estimulación suficiente como para desencadenar en el aprendiz una adaptación a un nuevo nivel de rendimiento. En base a esta propuesta, se relacionan los principios del entrenamiento deportivo con el aprendizaje de habilidades motrices. Se formula una perspectiva teórica que trata de explicar de forma común los procesos de modificación de los patrones motores independientemente del nivel de complejidad, conllevando los mismos

  14. Developing a methodology to predict PM10 concentrations in urban areas using generalized linear models.

    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.

  15. Fast inference in generalized linear models via expected log-likelihoods.

    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. A General Framework for Sequential and Adaptive Methods in Survival Studies

    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...

  17. Assessing the tangent linear behaviour of common tracer transport schemes and their use in a linearised atmospheric general circulation model

    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.

  18. Continuous dependence of solutions of abstract generalized linear differential equations with potential converging uniformly with a weight

    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...

  19. Adaptive Kronrod-Patterson integration of non-linear finite-element matrices

    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 ...

  20. Adaptive-Anisotropic Wavelet Collocation Method on general curvilinear coordinate systems

    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.

  1. Lin-Kernighan Heuristic Adaptation for the Generalized Traveling Salesman Problem

    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...

  2. Impact of co-channel interference on the performance of adaptive generalized transmit beamforming

    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.

  3. Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

    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.

  4. A smart rotor configuration with linear quadratic control of adaptive trailing edge flaps for active load alleviation

    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...

  5. A Memristor-Based Hyperchaotic Complex Lü System and Its Adaptive Complex Generalized Synchronization

    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.

  6. Adaptive neural control of non-affine pure-feedback non-linear systems with input nonlinearity and perturbed uncertainties

    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.

  7. A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints.

    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.

  8. Adaptive adjustment of the generalization-discrimination balance in larval Drosophila.

    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.

  9. Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.

    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.

  10. Protein structure validation by generalized linear model root-mean-square deviation prediction.

    Bagaria, Anurag; Jaravine, Victor; Huang, Yuanpeng J; Montelione, Gaetano T; Güntert, Peter

    2012-02-01

    Large-scale initiatives for obtaining spatial protein structures by experimental or computational means have accentuated the need for the critical assessment of protein structure determination and prediction methods. These include blind test projects such as the critical assessment of protein structure prediction (CASP) and the critical assessment of protein structure determination by nuclear magnetic resonance (CASD-NMR). An important aim is to establish structure validation criteria that can reliably assess the accuracy of a new protein structure. Various quality measures derived from the coordinates have been proposed. A universal structural quality assessment method should combine multiple individual scores in a meaningful way, which is challenging because of their different measurement units. Here, we present a method based on a generalized linear model (GLM) that combines diverse protein structure quality scores into a single quantity with intuitive meaning, namely the predicted coordinate root-mean-square deviation (RMSD) value between the present structure and the (unavailable) "true" structure (GLM-RMSD). For two sets of structural models from the CASD-NMR and CASP projects, this GLM-RMSD value was compared with the actual accuracy given by the RMSD value to the corresponding, experimentally determined reference structure from the Protein Data Bank (PDB). The correlation coefficients between actual (model vs. reference from PDB) and predicted (model vs. "true") heavy-atom RMSDs were 0.69 and 0.76, for the two datasets from CASD-NMR and CASP, respectively, which is considerably higher than those for the individual scores (-0.24 to 0.68). The GLM-RMSD can thus predict the accuracy of protein structures more reliably than individual coordinate-based quality scores.

  11. Generalized functional linear models for gene-based case-control association studies.

    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.

  12. Linear adaptive noise-reduction filters for tomographic imaging: Optimizing for minimum mean square error

    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.

  13. Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

    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.

  14. Minerals detection for hyperspectral images using adapted linear unmixing: LinMin

    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 ...

  15. Adaptions of ArcGIS' Linear Referencing System to the Coastal Environment

    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....

  16. Assessing the Tangent Linear Behaviour of Common Tracer Transport Schemes and Their Use in a Linearised Atmospheric General Circulation Model

    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.

  17. Dynamic Wavelength and Bandwidth Allocation Using Adaptive Linear Prediction in WDM/TDM Ethernet Passive Optical Networks

    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.

  18. High Order A-stable Continuous General Linear Methods for Solution of Systems of Initial Value Problems in ODEs

    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.

  19. Comparative Analysis of Linear and Nonlinear Pattern Synthesis of Hemispherical Antenna Array Using Adaptive Evolutionary Techniques

    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.

  20. Power Allocation Optimization: Linear Precoding Adapted to NB-LDPC Coded MIMO Transmission

    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.

  1. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    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...

  2. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    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...

  3. Use of the multinomial jack-knife and bootstrap in generalized non-linear canonical correlation analysis

    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

  4. Generalized Monge-Kantorovich optimization for grid generation and adaptation in LP

    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.

  5. Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging.

    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.

  6. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    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.

  7. Adaptive vision-based control of an unmanned aerial vehicle without linear velocity measurements.

    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.

  8. Adaptive Digital Predistortion with Iterative Noise Cancelation for Power Amplifier Linearization

    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.

  9. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

    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

  10. Neuro-Self Tuning Adaptive Controller for Non-Linear Dynamical Systems

    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.

  11. Linear systems modeling of adaptive optics in the spatial-frequency domain.

    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.

  12. Adaptive change of basis in entropy-based moment closures for linear kinetic equations

    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...

  13. A generalized hybrid transfinite element computational approach for nonlinear/linear unified thermal/structural analysis

    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.

  14. Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects.

    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/.

  15. Adaptive finite element modeling of direct current resistivity in 2-D generally anisotropic structures

    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.

  16. General up regulation of Spodoptera frugiperda trypsins and chymotrypsins allows its adaptation to soybean proteinase inhibitor.

    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.

  17. Major depressive disorder in the general hospital: adaptation of clinical practice guidelines.

    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.

  18. Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

    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.

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

    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

  20. Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models

    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...

  1. Some Numerical Methods for Exponential Analysis with Connection to a General Identification Scheme for Linear Processes

    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

  2. Continuity and general perturbation of the Drazin inverse for closed linear operators

    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.

  3. AN ACCURATE SOLUTION OF THE LINEAR THEORY OF THE WIND-DRIVEN OCEAN CIRCULATION-I. THE GENERALIZED SOLUTION

    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.

  4. Binary System with Components of Different Masses in the Linear Regime of the Characteristic Formulation of General Relativity

    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

    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. LINEAR STIELTJES EQUATION WITH GENERALIZED RIEMANN INTEGRAL AND EXISTENCE OF REGULATED SOLUTIONS

    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.

  7. Identification of general linear relationships between activation energies and enthalpy changes for dissociation reactions at surfaces.

    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.

  8. Generalized Coherent States of a Particle in a Time-Dependent Linear Potential

    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)>.

  9. Sparse non-linear denoising: Generalization performance and pattern reproducibility in functional MRI

    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...

  10. Examining secular trend  and seasonality in count data using dynamic generalized linear modelling

    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. GENERAL CAUCHY PROBLEM FOR THE LINEAR SHALLOW -WATER EQUATIONS ON AN EQUATORIAL BETA-PLANE

    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.

  12. General theory of spherically symmetric boundary-value problems of the linear transport theory.

    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.

  13. Extending generalized linear models with random effects and components of dispersion.

    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

  14. Size-extensive wave functions for quantum Monte Carlo: A linear scaling generalized valence bond approach

    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

  15. Strain Mediated Adaptation Is Key for Myosin Mechanochemistry: Discovering General Rules for Motor Activity.

    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.

  16. A general hybrid radiation transport scheme for star formation simulations on an adaptive grid

    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...

  17. Iterative solution of general sparse linear systems on clusters of workstations

    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.

  18. General polynomial factorization-based design of sparse periodic linear arrays.

    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.

  19. A Generalized Multiscale Finite Element Method for Poroelasticity Problems I: Linear Problems

    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...

  20. A Generalized Linear Transport Model for Spatially-Correlated Stochastic Media

    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...

  1. Approximation of Conjugate Functions by General Linear Operators of Their Fourier Series at the Lebesgue Points

    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].

  2. Cross-Cultural adaptation of the General Functioning Scale of the Family

    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.

  3. Fast adaptive principal component extraction based on a generalized energy function

    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.

  4. A Generalization of Palmer's Linearization Theorem%Palmer线性化定理的一个推广

    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

    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. Integer-linear-programing optimization in scalable video multicast with adaptive modulation and coding in wireless networks.

    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.

  7. Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

    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.

  8. Integer-Linear-Programing Optimization in Scalable Video Multicast with Adaptive Modulation and Coding in Wireless Networks

    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

  9. Combining support vector machines with linear quadratic regulator adaptation for the online design of an automotive active suspension system

    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.

  10. FPGA/NIOS Implementation of an Adaptive FIR Filter Using Linear Prediction to Reduce Narrow-Band RFI for Radio Detection of Cosmic Rays

    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

  11. A Priori Error Estimates of Mixed Finite Element Methods for General Linear Hyperbolic Convex Optimal Control Problems

    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.

  12. Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model

    Sedighi, Vahid; Fridrich, Jessica; Cogranne, Rémi

    2015-03-01

    The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.

  13. Robust Adaptive Sliding Mode Control for Generalized Function Projective Synchronization of Different Chaotic Systems with Unknown Parameters

    Xiuchun Li

    2013-01-01

    Full Text Available When the parameters of both drive and response systems are all unknown, an adaptive sliding mode controller, strongly robust to exotic perturbations, is designed for realizing generalized function projective synchronization. Sliding mode surface is given and the controlled system is asymptotically stable on this surface with the passage of time. Based on the adaptation laws and Lyapunov stability theory, an adaptive sliding controller is designed to ensure the occurrence of the sliding motion. Finally, numerical simulations are presented to verify the effectiveness and robustness of the proposed method even when both drive and response systems are perturbed with external disturbances.

  14. Parameter estimation of linear and quadratic chirps by employing the fractional fourier transform and a generalized time frequency transform

    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.

  15. Point particle binary system with components of different masses in the linear regime of the characteristic formulation of general relativity

    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.

  16. An algorithm for the construction of substitution box for block ciphers based on projective general linear group

    Altaleb, Anas; Saeed, Muhammad Sarwar; Hussain, Iqtadar; Aslam, Muhammad

    2017-03-01

    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.

  17. An algorithm for the construction of substitution box for block ciphers based on projective general linear group

    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.

  18. Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging

    Fowler, Michael James [Clarkson Univ., Potsdam, NY (United States)

    2014-04-25

    In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy

  19. Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model.

    Valeri, Linda; Lin, Xihong; VanderWeele, Tyler J

    2014-12-10

    Mediation analysis is a popular approach to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. When the mediator is mis-measured, the validity of mediation analysis can be severely undermined. In this paper, we first study the bias of classical, non-differential measurement error on a continuous mediator in the estimation of direct and indirect causal effects in generalized linear models when the outcome is either continuous or discrete and exposure-mediator interaction may be present. Our theoretical results as well as a numerical study demonstrate that in the presence of non-linearities, the bias of naive estimators for direct and indirect effects that ignore measurement error can take unintuitive directions. We then develop methods to correct for measurement error. Three correction approaches using method of moments, regression calibration, and SIMEX are compared. We apply the proposed method to the Massachusetts General Hospital lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk.

  20. General expressions for R1ρ relaxation for N-site chemical exchange and the special case of linear chains

    Koss, Hans; Rance, Mark; Palmer, Arthur G.

    2017-01-01

    Exploration of dynamic processes in proteins and nucleic acids by spin-locking NMR experiments has been facilitated by the development of theoretical expressions for the R1ρ relaxation rate constant covering a variety of kinetic situations. Herein, we present a generalized approximation to the chemical exchange, Rex, component of R1ρ for arbitrary kinetic schemes, assuming the presence of a dominant major site population, derived from the negative reciprocal trace of the inverse Bloch-McConnell evolution matrix. This approximation is equivalent to first-order truncation of the characteristic polynomial derived from the Bloch-McConnell evolution matrix. For three- and four-site chemical exchange, the first-order approximations are sufficient to distinguish different kinetic schemes. We also introduce an approach to calculate R1ρ for linear N-site schemes, using the matrix determinant lemma to reduce the corresponding 3N × 3N Bloch-McConnell evolution matrix to a 3 × 3 matrix. The first- and second order-expansions of the determinant of this 3 × 3 matrix are closely related to previously derived equations for two-site exchange. The second-order approximations for linear N-site schemes can be used to obtain more accurate approximations for non-linear N-site schemes, such as triangular three-site or star four-site topologies. The expressions presented herein provide powerful means for the estimation of Rex contributions for both low (CEST-limit) and high (R1ρ-limit) radiofrequency field strengths, provided that the population of one state is dominant. The general nature of the new expressions allows for consideration of complex kinetic situations in the analysis of NMR spin relaxation data.

  1. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

    Faezehossadat Khademi

    2016-12-01

    Full Text Available Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS, and Multiple Linear Regression (MLR were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC. Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

  2. What do we call Adaptive Management? A general characterization from a global sample

    T. Espigares

    2008-03-01

    Full Text Available This study presents a characterisation of the implementation of Adaptive Management (AM from the analysis of 35 projects around the world. Our results reveal that AM projects are usually aimed at ecosystem management, conservation and restoration. Also, they mainly act upon forest or epicontinental water ecosystems and their goal is generally species exploitation and in most cases these projects act at a local scale. From a methodological point of view, most AM cases use an active approach and monitoring programs and were at the phase of problem identification. We found differences in the implementation of AM between developed and developing countries that were present in our samples in the following way: AM projects in developed countries were typically carried out by state agencies, and focused on solving problems concerning epicontinental waters and the public use of ecosystems. They had the support of national funds and used modelling techniques. In contrast, the AM projects from developing countries were mainly aimed at the conservation of natural protected areas and at the mitigation of environmental impacts derived from mining activities. The financial support of these projects was frequently provided by international organizations, and the use of modelling techniques was uncommon. For a better exploitation of all the possibilities of AM, we suggest the use of criteria to be customized to the specific needs of the socio-economic reality of every country and to monitor the results at a global scale to continuously improve this practice.

  3. Foam Multi-Dimensional General Purpose Monte Carlo Generator With Self-Adapting Symplectic Grid

    Jadach, Stanislaw

    2000-01-01

    A new general purpose Monte Carlo event generator with self-adapting grid consisting of simplices is described. In the process of initialization, the simplex-shaped cells divide into daughter subcells in such a way that: (a) cell density is biggest in areas where integrand is peaked, (b) cells elongate themselves along hyperspaces where integrand is enhanced/singular. The grid is anisotropic, i.e. memory of the axes directions of the primary reference frame is lost. In particular, the algorithm is capable of dealing with distributions featuring strong correlation among variables (like ridge along diagonal). The presented algorithm is complementary to others known and commonly used in the Monte Carlo event generators. It is, in principle, more effective then any other one for distributions with very complicated patterns of singularities - the price to pay is that it is memory-hungry. It is therefore aimed at a small number of integration dimensions (<10). It should be combined with other methods for higher ...

  4. A general hybrid radiation transport scheme for star formation simulations on an adaptive grid

    Klassen, Mikhail; Pudritz, Ralph E. [Department of Physics and Astronomy, McMaster University 1280 Main Street W, Hamilton, ON L8S 4M1 (Canada); Kuiper, Rolf [Max Planck Institute for Astronomy Königstuhl 17, D-69117 Heidelberg (Germany); Peters, Thomas [Institut für Computergestützte Wissenschaften, Universität Zürich Winterthurerstrasse 190, CH-8057 Zürich (Switzerland); Banerjee, Robi; Buntemeyer, Lars, E-mail: klassm@mcmaster.ca [Hamburger Sternwarte, Universität Hamburg Gojenbergsweg 112, D-21029 Hamburg (Germany)

    2014-12-10

    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 magnetohydrodyanmics 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 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 calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.

  5. A General Hybrid Radiation Transport Scheme for Star Formation Simulations on an Adaptive Grid

    Klassen, Mikhail; Kuiper, Rolf; Pudritz, Ralph E.; Peters, Thomas; Banerjee, Robi; Buntemeyer, Lars

    2014-12-01

    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 magnetohydrodyanmics 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 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 calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.

  6. A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation.

    Molloy, Kevin; Shehu, Amarda

    2016-03-01

    Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

  7. Impact of co-channel interference on the performance of adaptive non-ideal generalized transmit diversity

    Radaydeh, Redha Mahmoud Mesleh

    2010-09-01

    The impact of co-channel interference and nonideal estimation of the desired user channel state information (CSI) on the performance of an adaptive threshold-based generalized transmit diversity for low-complexity multiple-input single-output configuration is investigated. The adaptation to channel conditions is assumed to be based on the desired user CSI, and the number of active transmit antennas is adjusted accordingly to guarantee predetermined target performance. To facilitate comparisons between different adaptation schemes, new analytical results for the statistics of combined signal-to-interference-plus-noise ratio (SINR) are derived, which can be applied for different fading conditions of interfering signals. Selected numerical results are presented to validate the analytical development and to compare the outage performance of the considered adaptation schemes. © 2010 IEEE.

  8. Adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions

    Dai Hao; Jia Li-Xin; Zhang Yan-Bin

    2012-01-01

    The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper.Based on Lyapunov stability theory and Barbalat's lemma,generalized matrix projective lag synchronization criteria are derived by using the adaptive control method.Furthermore,each network can be undirected or directed,connected or disconnected,and nodes in either network may have identical or different dynamics.The proposed strategy is applicable to almost all kinds of complex networks.In addition,numerical simulation results are presented to illustrate the effectiveness of this method,showing that the synchronization speed is sensitively influenced by the adaptive law strength,the network size,and the network topological structure.

  9. A generalized electrostatic micro-mirror (GEM) model for a two-axis convex piecewise linear shaped MEMS mirror

    Edwards, C. L.; Edwards, M. L.

    2009-05-01

    MEMS micro-mirror technology offers the opportunity to replace larger optical actuators with smaller, faster ones for lidar, network switching, and other beam steering applications. Recent developments in modeling and simulation of MEMS two-axis (tip-tilt) mirrors have resulted in closed-form solutions that are expressed in terms of physical, electrical and environmental parameters related to the MEMS device. The closed-form analytical expressions enable dynamic time-domain simulations without excessive computational overhead and are referred to as the Micro-mirror Pointing Model (MPM). Additionally, these first-principle models have been experimentally validated with in-situ static, dynamic, and stochastic measurements illustrating their reliability. These models have assumed that the mirror has a rectangular shape. Because the corners can limit the dynamic operation of a rectangular mirror, it is desirable to shape the mirror, e.g., mitering the corners. Presented in this paper is the formulation of a generalized electrostatic micromirror (GEM) model with an arbitrary convex piecewise linear shape that is readily implemented in MATLAB and SIMULINK for steady-state and dynamic simulations. Additionally, such a model permits an arbitrary shaped mirror to be approximated as a series of linearly tapered segments. Previously, "effective area" arguments were used to model a non-rectangular shaped mirror with an equivalent rectangular one. The GEM model shows the limitations of this approach and provides a pre-fabrication tool for designing mirror shapes.

  10. Limit of ratio of consecutive terms for general order-k linear homogeneous recurrences with constant coefficients

    Fiorenza, Alberto, E-mail: fiorenza@unina.i [Dipartimento di Costruzioni e Metodi Matematici in Architettura, Universita di Napoli, Via Monteoliveto, 3, I-80134 Napoli (Italy); Istituto per le Applicazioni del Calcolo ' Mauro Picone' , Sezione di Napoli, Consiglio Nazionale delle Ricerche, via Pietro Castellino, 111, I-80131 Napoli (Italy); Vincenzi, Giovanni, E-mail: vincenzi@unisa.i [Dipartimento di Matematica, Universita di Salerno, via Ponte Don Melillo, 4, I-84084 Fisciano, Salerno (Italy)

    2011-01-15

    Research highlights: We prove a result true for all linear homogeneous recurrences with constant coefficients. As a corollary of our results we immediately get the celebrated Poincare' theorem. The limit of the ratio of adjacent terms is characterized as the unique leading root of the characteristic polynomial. The Golden Ratio, Kepler limit of the classical Fibonacci sequence, is the unique leading root. The Kepler limit may differ from the unique root of maximum modulus and multiplicity. - Abstract: For complex linear homogeneous recursive sequences with constant coefficients we find a necessary and sufficient condition for the existence of the limit of the ratio of consecutive terms. The result can be applied even if the characteristic polynomial has not necessarily roots with modulus pairwise distinct, as in the celebrated Poincare's theorem. In case of existence, we characterize the limit as a particular root of the characteristic polynomial, which depends on the initial conditions and that is not necessarily the unique root with maximum modulus and multiplicity. The result extends to a quite general context the way used to find the Golden mean as limit of ratio of consecutive terms of the classical Fibonacci sequence.

  11. A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

    Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi

    2015-01-01

    The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…

  12. Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35

    Xu, Xueli; von Davier, Matthias

    2008-01-01

    The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume…

  13. SAS macro programs for geographically weighted generalized linear modeling with spatial point data: applications to health research.

    Chen, Vivian Yi-Ju; Yang, Tse-Chuan

    2012-08-01

    An increasing interest in exploring spatial non-stationarity has generated several specialized analytic software programs; however, few of these programs can be integrated natively into a well-developed statistical environment such as SAS. We not only developed a set of SAS macro programs to fill this gap, but also expanded the geographically weighted generalized linear modeling (GWGLM) by integrating the strengths of SAS into the GWGLM framework. Three features distinguish our work. First, the macro programs of this study provide more kernel weighting functions than the existing programs. Second, with our codes the users are able to better specify the bandwidth selection process compared to the capabilities of existing programs. Third, the development of the macro programs is fully embedded in the SAS environment, providing great potential for future exploration of complicated spatially varying coefficient models in other disciplines. We provided three empirical examples to illustrate the use of the SAS macro programs and demonstrated the advantages explained above.

  14. First principles analysis of the Abraham-Minkowski controversy for the momentum of light in general linear media

    Ramos, Tomás; Obukhov, Yuri N

    2013-01-01

    We study the problem of the definition of the energy-momentum tensor of light in general moving media with linear constitutive law. Using the basic principles of classical field theory, we show that for the correct understanding of the problem, one needs to carefully distinguish situations when the material medium is modeled either as a background on which light propagates or as a dynamical part of the total system. In the former case, we prove that the (generalized) Belinfante-Rosenfeld (BR) tensor for the electromagnetic field coincides with the Minkowski tensor. We derive a complete set of balance equations for this open system and show that the symmetries of the background medium are directly related to the conservation of the Minkowski quantities. In particular, for isotropic media, the angular momentum of light is conserved despite of the fact that the Minkowski tensor is non-symmetric. For the closed system of light interacting with matter, we model the material medium as a relativistic non-dissipative...

  15. Model-free Adaptive Control With Tight Format of Linear Motor%直线电机的紧格式无模型自适应控制

    李萍; 曹健

    2014-01-01

    将基于紧格式线性化的非线性系统无模型自适应控制方法应用于直线电机的控制中畅利用伪偏导数和伪阶数的概念,用紧格式动态线性时变模型替代直线电机非线性系统模型畅根据直线电机运动模型的输入输出数据在线估计系统的伪偏导数。仿真实验表明,紧格式无模型自适应控制器对电机这种具有不确知动态的非线性系统有较强的自适应性、抗干扰性、稳定性和鲁棒性,解决了直线电机非线性和不确定性的控制问题。%The model-free adaptive control ( MFAC) approach of nonlinear systems based on linearization of tight format was applied to control of the linear motor.Used the concept of pseudo partial derivative and pseudo order , nonlinear system model of linear motor was replaced with tight format dynamic linear time-var-ying model.According to the linear motor motion model of input and output data online estimation of pseudo partial derivative.The simulation results show that tight format model-free controller has a strong adaptive , anti-interference , stable and robustness for motor with vaguely known dynamic nonlinear systems , solved the problem of controlling the nonlinear and uncertainty of linear motor.

  16. Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework.

    Andersen, A H; Gash, D M; Avison, M J

    1999-07-01

    Principal component analysis (PCA) is one of several structure-seeking multivariate statistical techniques, exploratory as well as inferential, that have been proposed recently for the characterization and detection of activation in both PET and fMRI time series data. In particular, PCA is data driven and does not assume that the neural or hemodynamic response reaches some steady state, nor does it involve correlation with any pre-defined or exogenous experimental design template. In this paper, we present a generalized linear systems framework for PCA based on the singular value decomposition (SVD) model for representation of spatio-temporal fMRI data sets. Statistical inference procedures for PCA, including point and interval estimation will be introduced without the constraint of explicit hypotheses about specific task-dependent effects. The principal eigenvectors capture both the spatial and temporal aspects of fMRI data in a progressive fashion; they are inherently matched to unique and uncorrelated features and are ranked in order of the amount of variance explained. PCA also acts as a variation reduction technique, relegating most of the random noise to the trailing components while collecting systematic structure into the leading ones. Features summarizing variability may not directly be those that are the most useful. Further analysis is facilitated through linear subspace methods involving PC rotation and strategies of projection pursuit utilizing a reduced, lower-dimensional natural basis representation that retains most of the information. These properties will be illustrated in the setting of dynamic time-series response data from fMRI experiments involving pharmacological stimulation of the dopaminergic nigro-striatal system in primates.

  17. Intracranial stereotactic radiosurgery with an adapted linear accelerator vs. robotic radiosurgery. Comparison of dosimetric treatment plan quality

    Treuer, Harald; Hoevels, Moritz; Luyken, Klaus; Visser-Vandewalle, Veerle; Wirths, Jochen; Ruge, Maximilian [University Hospital Cologne, Department of Stereotaxy and Functional Neurosurgery, Cologne (Germany); Kocher, Martin [University Hospital Cologne, Department of Radiotherapy, Cologne (Germany)

    2014-11-22

    Stereotactic radiosurgery with an adapted linear accelerator (linac-SRS) is an established therapy option for brain metastases, benign brain tumors, and arteriovenous malformations. We intended to investigate whether the dosimetric quality of treatment plans achieved with a CyberKnife (CK) is at least equivalent to that for linac-SRS with circular or micromultileaf collimators (microMLC). A random sample of 16 patients with 23 target volumes, previously treated with linac-SRS, was replanned with CK. Planning constraints were identical dose prescription and clinical applicability. In all cases uniform optimization scripts and inverse planning objectives were used. Plans were compared with respect to coverage, minimal dose within target volume, conformity index, and volume of brain tissue irradiated with ≥ 10 Gy. Generating the CK plan was unproblematic with simple optimization scripts in all cases. With the CK plans, coverage, minimal target volume dosage, and conformity index were significantly better, while no significant improvement could be shown regarding the 10 Gy volume. Multiobjective comparison for the irradiated target volumes was superior in the CK plan in 20 out of 23 cases and equivalent in 3 out of 23 cases. Multiobjective comparison for the treated patients was superior in the CK plan in all 16 cases. The results clearly demonstrate the superiority of the irradiation plan for CK compared to classical linac-SRS with circular collimators and microMLC. In particular, the average minimal target volume dose per patient, increased by 1.9 Gy, and at the same time a 14 % better conformation index seems to be an improvement with clinical relevance. (orig.) [German] Stereotaktische Radiochirurgie mit einem adaptierten Linearbeschleuniger (Linac-SRS) ist eine erfolgreiche und etablierte Therapieoption fuer Hirnmetastasen, benigne Hirntumoren und arteriovenoese Malformationen. Ziel war es, zu untersuchen, ob die mit einem CyberKnife (CK) erreichbare

  18. Use of reflectance spectrophotometry and colorimetry in a general linear model for the determination of the age of bruises.

    Hughes, Vanessa K; Langlois, Neil E I

    2010-12-01

    Bruises can have medicolegal significance such that the age of a bruise may be an important issue. This study sought to determine if colorimetry or reflectance spectrophotometry could be employed to objectively estimate the age of bruises. Based on a previously described method, reflectance spectrophotometric scans were obtained from bruises using a Cary 100 Bio spectrophotometer fitted with a fibre-optic reflectance probe. Measurements were taken from the bruise and a control area. Software was used to calculate the first derivative at 490 and 480 nm; the proportion of oxygenated hemoglobin was calculated using an isobestic point method and a software application converted the scan data into colorimetry data. In addition, data on factors that might be associated with the determination of the age of a bruise: subject age, subject sex, degree of trauma, bruise size, skin color, body build, and depth of bruise were recorded. From 147 subjects, 233 reflectance spectrophotometry scans were obtained for analysis. The age of the bruises ranged from 0.5 to 231.5 h. A General Linear Model analysis method was used. This revealed that colorimetric measurement of the yellowness of a bruise accounted for 13% of the bruise age. By incorporation of the other recorded data (as above), yellowness could predict up to 32% of the age of a bruise-implying that 68% of the variation was dependent on other factors. However, critical appraisal of the model revealed that the colorimetry method of determining the age of a bruise was affected by skin tone and required a measure of the proportion of oxygenated hemoglobin, which is obtained by spectrophotometric methods. Using spectrophotometry, the first derivative at 490 nm alone accounted for 18% of the bruise age estimate. When additional factors (subject sex, bruise depth and oxygenation of hemoglobin) were included in the General Linear Model this increased to 31%-implying that 69% of the variation was dependent on other factors. This

  19. General adaptive-neighborhood technique for improving synthetic aperture radar interferometric coherence estimation.

    Vasile, Gabriel; Trouvé, Emmanuel; Ciuc, Mihai; Buzuloiu, Vasile

    2004-08-01

    A new method for filtering the coherence map issued from synthetic aperture radar (SAR) interferometric data is presented. For each pixel of the interferogram, an adaptive neighborhood is determined by a region-growing technique driven by the information provided by the amplitude images. Then pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after a phase-compensation step is performed. An extension of the algorithm is proposed for polarimetric interferometric SAR images. The proposed method has been applied to both European Remote Sensing (ERS) satellite SAR images and airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multilook filter and the Lee adaptive coherence filter.

  20. Frequency-adaptive grid-virtual-flux synchronization by multiple second-order generalized integrators under distorted grid conditions

    2015-01-01

    With some of the intermittent new energy and large nonlinear loads, grid voltage unbalance, harmonics, and frequency deviation are increasing year by year. The voltage source converter (VSC) is seriously affected by the various unexpected factors, and the presence of grid impedance makes the situation worse. In order to make the VSC track the nonideal grid quickly and accurately, this paper proposes a frequency-adaptive grid-virtual-flux synchronization by multiple second-order generalized in...

  1. Robust master-slave synchronization for general uncertain delayed dynamical model based on adaptive control scheme.

    Wang, Tianbo; Zhou, Wuneng; Zhao, Shouwei; Yu, Weiqin

    2014-03-01

    In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method.

  2. Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers

    Cyril R Pernet

    2014-01-01

    Full Text Available This tutorial presents several misconceptions related to the use the General Linear Model (GLM in functional Magnetic Resonance Imaging (fMRI. The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab. In particular, I address issues related to (i model parameterization (modelling baseline or null events and scaling of the design matrix; (ii hemodynamic modelling using basis functions, and (iii computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (model over-parameterization, and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the haemodynamic model (haemodynamic function only or using derivatives can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analysis and give some recommendations.

  3. SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models.

    Vock, David M; Davidian, Marie; Tsiatis, Anastasios A

    2014-01-01

    Generalized linear and nonlinear mixed models (GMMMs and NLMMs) are commonly used to represent non-Gaussian or nonlinear longitudinal or clustered data. A common assumption is that the random effects are Gaussian. However, this assumption may be unrealistic in some applications, and misspecification of the random effects density may lead to maximum likelihood parameter estimators that are inconsistent, biased, and inefficient. Because testing if the random effects are Gaussian is difficult, previous research has recommended using a flexible random effects density. However, computational limitations have precluded widespread use of flexible random effects densities for GLMMs and NLMMs. We develop a SAS macro, SNP_NLMM, that overcomes the computational challenges to fit GLMMs and NLMMs where the random effects are assumed to follow a smooth density that can be represented by the seminonparametric formulation proposed by Gallant and Nychka (1987). The macro is flexible enough to allow for any density of the response conditional on the random effects and any nonlinear mean trajectory. We demonstrate the SNP_NLMM macro on a GLMM of the disease progression of toenail infection and on a NLMM of intravenous drug concentration over time.

  4. A General Robust Linear Transceiver Design for Multi-Hop Amplify-and-Forward MIMO Relaying Systems

    Xing, Chengwen; Ma, Shaodan; Fei, Zesong; Wu, Yik-Chung; Poor, H. Vincent

    2013-03-01

    In this paper, linear transceiver design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with Gaussian distributed channel estimation errors is investigated. Commonly used transceiver design criteria including weighted mean-square-error (MSE) minimization, capacity maximization, worst-MSE/MAX-MSE minimization and weighted sum-rate maximization, are considered and unified into a single matrix-variate optimization problem. A general robust design algorithm is proposed to solve the unified problem. Specifically, by exploiting majorization theory and properties of matrix-variate functions, the optimal structure of the robust transceiver is derived when either the covariance matrix of channel estimation errors seen from the transmitter side or the corresponding covariance matrix seen from the receiver side is proportional to an identity matrix. Based on the optimal structure, the original transceiver design problems are reduced to much simpler problems with only scalar variables whose solutions are readily obtained by iterative water-filling algorithm. A number of existing transceiver design algorithms are found to be special cases of the proposed solution. The differences between our work and the existing related work are also discussed in detail. The performance advantages of the proposed robust designs are demonstrated by simulation results.

  5. Complex-number representation of informed basis functions in general linear modeling of Functional Magnetic Resonance Imaging.

    Wang, Pengwei; Wang, Zhishun; He, Lianghua

    2012-03-30

    Functional Magnetic Resonance Imaging (fMRI), measuring Blood Oxygen Level-Dependent (BOLD), is a widely used tool to reveal spatiotemporal pattern of neural activity in human brain. Standard analysis of fMRI data relies on a general linear model and the model is constructed by convolving the task stimuli with a hypothesized hemodynamic response function (HRF). To capture possible phase shifts in the observed BOLD response, the informed basis functions including canonical HRF and its temporal derivative, have been proposed to extend the hypothesized hemodynamic response in order to obtain a good fitting model. Different t contrasts are constructed from the estimated model parameters for detecting the neural activity between different task conditions. However, the estimated model parameters corresponding to the orthogonal basis functions have different physical meanings. It remains unclear how to combine the neural features detected by the two basis functions and construct t contrasts for further analyses. In this paper, we have proposed a novel method for representing multiple basis functions in complex domain to model the task-driven fMRI data. Using this method, we can treat each pair of model parameters, corresponding respectively to canonical HRF and its temporal derivative, as one complex number for each task condition. Using the specific rule we have defined, we can conveniently perform arithmetical operations on the estimated model parameters and generate different t contrasts. We validate this method using the fMRI data acquired from twenty-two healthy participants who underwent an auditory stimulation task.

  6. General characterization of Tityus fasciolatus scorpion venom. Molecular identification of toxins and localization of linear B-cell epitopes.

    Mendes, T M; Guimarães-Okamoto, P T C; Machado-de-Avila, R A; Oliveira, D; Melo, M M; Lobato, Z I; Kalapothakis, E; Chávez-Olórtegui, C

    2015-06-01

    This communication describes the general characteristics of the venom from the Brazilian scorpion Tityus fasciolatus, which is an endemic species found in the central Brazil (States of Goiás and Minas Gerais), being responsible for sting accidents in this area. The soluble venom obtained from this scorpion is toxic to mice being the LD50 is 2.984 mg/kg (subcutaneally). SDS-PAGE of the soluble venom resulted in 10 fractions ranged in size from 6 to 10-80 kDa. Sheep were employed for anti-T. fasciolatus venom serum production. Western blotting analysis showed that most of these venom proteins are immunogenic. T. fasciolatus anti-venom revealed consistent cross-reactivity with venom antigens from Tityus serrulatus. Using known primers for T. serrulatus toxins, we have identified three toxins sequences from T. fasciolatus venom. Linear epitopes of these toxins were localized and fifty-five overlapping pentadecapeptides covering complete amino acid sequence of the three toxins were synthesized in cellulose membrane (spot-synthesis technique). The epitopes were located on the 3D structures and some important residues for structure/function were identified.

  7. Projected changes in precipitation and temperature over the Canadian Prairie Provinces using the Generalized Linear Model statistical downscaling approach

    Asong, Z. E.; Khaliq, M. N.; Wheater, H. S.

    2016-08-01

    In this study, a multisite multivariate statistical downscaling approach based on the Generalized Linear Model (GLM) framework is developed to downscale daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. First, large scale atmospheric covariates from the National Center for Environmental Prediction (NCEP) Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate GLMs for the 1971-2000 period. Then the calibrated models are used to generate daily sequences of precipitation and temperature for the 1962-2005 historical (conditioned on NCEP predictors), and future period (2006-2100) using outputs from five CMIP5 (Coupled Model Intercomparison Project Phase-5) Earth System Models corresponding to Representative Concentration Pathway (RCP): RCP2.6, RCP4.5, and RCP8.5 scenarios. The results indicate that the fitted GLMs are able to capture spatiotemporal characteristics of observed precipitation and temperature fields. According to the downscaled future climate, mean precipitation is projected to increase in summer and decrease in winter while minimum temperature is expected to warm faster than the maximum temperature. Climate extremes are projected to intensify with increased radiative forcing.

  8. A Community Needs Index for Adolescent Pregnancy Prevention Program Planning: Application of Spatial Generalized Linear Mixed Models.

    Johnson, Glen D; Mesler, Kristine; Kacica, Marilyn A

    2017-02-06

    Objective The objective is to estimate community needs with respect to risky adolescent sexual behavior in a way that is risk-adjusted for multiple community factors. Methods Generalized linear mixed modeling was applied for estimating teen pregnancy and sexually transmitted disease (STD) incidence by postal ZIP code in New York State, in a way that adjusts for other community covariables and residual spatial autocorrelation. A community needs index was then obtained by summing the risk-adjusted estimates of pregnancy and STD cases. Results Poisson regression with a spatial random effect was chosen among competing modeling approaches. Both the risk-adjusted caseloads and rates were computed for ZIP codes, which allowed risk-based prioritization to help guide funding decisions for a comprehensive adolescent pregnancy prevention program. Conclusions This approach provides quantitative evidence of community needs with respect to risky adolescent sexual behavior, while adjusting for other community-level variables and stabilizing estimates in areas with small populations. Therefore, it was well accepted by the affected groups and proved valuable for program planning. This methodology may also prove valuable for follow up program evaluation. Current research is directed towards further improving the statistical modeling approach and applying to different health and behavioral outcomes, along with different predictor variables.

  9. Climateric: fatigue or third stage of the general adaptation syndrome Climaterio: fatiga o tercera etapa del síndrome de adaptación general

    William Alvarez Gaviria

    2004-01-01

    The origin of climacteric has been subject of debate. Most opinions agree in that it arises exclusively from natural selection. In this paper the author argues that, besides this reason there is another, even more important; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts and lack of response to insulin. From a genetic point of view, this corre...

  10. Adaptive Lasso for Poisson log-linear regression model%自适应Lasso在Poisson对数线性回归模型下的性质

    崔静; 郭鹏江; 夏志明

    2011-01-01

    Aim To study adaptive Lasso for Poisson log-linear regrersion model. Methods The methods of mathematical analysis and probability theory are used. Results Under some conditions, the adaptive Lasso estimator for Poisson log-linear regression has the oracle properties which are sparsity and asymptotic normality. Conclusion A-daptive Lasso can effectively choose variables for Poisson log-linar regression model and estimate the variable coefficient.%目的 研究自适应Lasso在Poisson对数线性模型下的性质.方法 利用数学分析及概率论中的性质.结果 证明了在Poisson对数线性模型下自适应Lasso估计量具有稀疏性和渐进正态性.结论 自适应Lasso可以有效选择Poisson对数线性模型中的变量,并同时估计变量系数.

  11. A fully general and adaptive inverse analysis method for cementitious materials

    Jepsen, Michael S.; Damkilde, Lars; Lövgren, Ingemar

    2016-01-01

    on least square fitting between test data obtained from various kinds of test setup, three-point bending or wedge splitting test, and simulated data obtained by either FEA or analytical models. In the current paper adaptive inverse analysis is conducted on test data obtained from three-point bending...

  12. Designing a Fuzzy Adaptive Controller for a Rigid joint Two Link Non-Linear Manipulator with Uncertainty

    Maryam Montazeri

    2013-01-01

    Full Text Available This paper presents a control approach to the fuzzy-adaptive control scheme for rigid manipulators with unknown parameters. Lagrange’s method is employed for computing robot motion dynamics. Stability analysis guaranteed through Lyapunov’s theory using some suitable adaptive rules that make sure all signals in the closed-loop system are bounded and tracking error ones asymptotically reaches to zero. Compared with other controllers, there are some numerical simulations that verify effectiveness of the proposed method. Also, simulation results verify that the proposed controller can deal with uncertainties in the system.

  13. Linear generalized synchronization of complex networks%复杂网络的线性广义同步

    卞秋香; 姚洪兴

    2011-01-01

    基于Lyapunov稳定性理论,研究了两个复杂网络的线性广义同步(LGS)问题.通过构造控制器实现了两个参数不确定时滞复杂网络的LGS,给定驱动网络以及线性映射,可以构造响应网络来实现LGS.结果可用于指导能源供求网络、金融网络等的平衡发展.以企业家激励网络及企业经济增长要素网络进行数值仿真,参数不确定也可实现两个网络的LGS,从而在一种和谐同步发展的状态下,能更好的实现企业经济的稳步发展.当企业家激励网络参数未知时,可构造响应网络来实现LGS,一方面可对该网络进行同步控制,达到预期的效果;另一方面可对参数进行辨识,确定网络结构.%The linear generalized synchronization (LGS) between two complex networks is investigated based on the Lyapunov stability theory. By constructing controller, two uncertain complex networks with time-delays can realize LGS. We can also construct a response network to realize LGS with the drive network and a given linear mapping. The results can be used to instruct the balanced development of networks, such as energy supply network, the financial network etc. Entrepreneurs stimulate network and enterprise economic growth factor network have been simulated. Under uncertain parameters, the LGS of the two networks can be obtained, so enterprise economy developing can be better steadily in a state of harmonious development. For the entrepreneurs stimulate network with unknown parameters, the response network can be constructed to realize LGS. One can control the network to achieve the synchronization for simultaneously identifying the unknown parameters to affirm the structure of the network.

  14. Generalized Linear Models to Identify Key Hydromorphological and Chemical Variables Determining the Occurrence of Macroinvertebrates in the Guayas River Basin (Ecuador

    Minar Naomi Damanik-Ambarita

    2016-07-01

    Full Text Available The biotic integrity of the Guayas River basin in Ecuador is at environmental risk due to extensive anthropogenic activities. We investigated the potential impacts of hydromorphological and chemical variables on biotic integrity using macroinvertebrate-based bioassessments. The bioassessment methods utilized included the Biological Monitoring Working Party adapted for Colombia (BMWP-Col and the average score per taxon (ASPT, via an extensive sampling campaign that was completed throughout the river basin at 120 sampling sites. The BMWP-Col classification ranged from very bad to good, and from probable severe pollution to clean water based on the ASPT scores. Generalized linear models (GLMs and sensitivity analysis were used to relate the bioassessment index to hydromorphological and chemical variables. It was found that elevation, nitrate-N, sediment angularity, logs, presence of macrophytes, flow velocity, turbidity, bank shape, land use and chlorophyll were the key environmental variables affecting the BMWP-Col. From the analyses, it was observed that the rivers at the upstream higher elevations of the river basin were in better condition compared to lowland systems and that a higher flow velocity was linked to a better BMWP-Col score. The nitrate concentrations were very low in the entire river basin and did not relate to a negative impact on the macroinvertebrate communities. Although the results of the models provided insights into the ecosystem, cross fold model development and validation also showed that there was a level of uncertainty in the outcomes. However, the results of the models and sensitivity analysis can support water management actions to determine and focus on alterable variables, such as the land use at different elevations, monitoring of nitrate and chlorophyll concentrations, macrophyte presence, sediment transport and bank stability.

  15. Weil Representation of a Generalized Linear Group over a Ring of Truncated Polynomials over a Finite Field Endowed with a Second Class Involution

    Frez, Luis Gutiérrez; Pantoja, José

    2015-01-01

    We construct a complex linear Weil representation $\\rho$ of the generalized special linear group $G={\\rm SL}_*^{1}(2,A_n)$ ($A_n=K[x]/\\langle x^n\\rangle$, $K$ the quadratic extension of the finite field $k$ of $q$ elements, $q$ odd), where $A_n$ is endowed with a second class involution. After the construction of a specific data, the representation is defined on the generators of a Bruhat presentation of $G$, via linear operators satisfying the relations of the presentation. The structure of ...

  16. Note: High frequency vibration rejection using a linear shaft actuator-based image stabilizing device via vestibulo-ocular reflex adaptation control method

    Koh, Doo-Yeol; Kim, Young-Kook; Kim, Kyung-Soo; Kim, Soohyun

    2013-08-01

    In mobile robotics, obtaining stable image of a mounted camera is crucial for operating a mobile system to complete given tasks. This note presents the development of a high-speed image stabilizing device using linear shaft actuator, and a new image stabilization method inspired by human gaze stabilization process known as vestibulo-ocular reflex (VOR). In the proposed control, the reference is adaptively adjusted by the VOR adaptation control to reject residual vibration of a camera as the VOR gain converges to optimal state. Through experiments on a pneumatic vibrator, it will be shown that the proposed system is capable of stabilizing 10 Hz platform vibration, which shows potential applicability of the device to a high-speed mobile robot.

  17. Development of flank wear model of cutting tool by using adaptive feedback linear control system on machining AISI D2 steel and AISI 4340 steel

    Orra, Kashfull; Choudhury, Sounak K.

    2016-12-01

    The purpose of this paper is to build an adaptive feedback linear control system to check the variation of cutting force signal to improve the tool life. The paper discusses the use of transfer function approach in improving the mathematical modelling and adaptively controlling the process dynamics of the turning operation. The experimental results shows to be in agreement with the simulation model and error obtained is less than 3%. The state space approach model used in this paper successfully check the adequacy of the control system through controllability and observability test matrix and can be transferred from one state to another by appropriate input control in a finite time. The proposed system can be implemented to other machining process under varying range of cutting conditions to improve the efficiency and observability of the system.

  18. Generating Initial Data in General Relativity using Adaptive Finite Element Methods

    Aksoylu, Burak; Bond, Stephen; Holst, Michael

    2008-01-01

    The conformal formulation of the Einstein constraint equations is first reviewed, and we then consider the design, analysis, and implementation of adaptive multilevel finite element-type numerical methods for the resulting coupled nonlinear elliptic system. We derive weak formulations of the coupled constraints, and review some new developments in the solution theory for the constraints in the cases of constant mean extrinsic curvature (CMC) data, near-CMC data, and arbitrarily prescribed mean extrinsic curvature data. We then outline some recent results on a priori and a posteriori error estimates for a broad class of Galerkin-type approximation methods for this system which includes techniques such as finite element, wavelet, and spectral methods. We then use these estimates to construct an adaptive finite element method (AFEM) for solving this system numerically, and outline some new convergence and optimality results. We then describe in some detail an implementation of the methods using the FETK software...

  19. Adaptive Continuous time Markov Chain Approximation Model to\\ud General Jump-Diffusions

    Cerrato, Mario; Lo, Chia Chun; Skindilias, Konstantinos

    2011-01-01

    We propose a non-equidistant Q rate matrix formula and an adaptive numerical algorithm for a continuous time Markov chain to approximate jump-diffusions with affine or non-affine functional specifications. Our approach also accommodates state-dependent jump intensity and jump distribution, a flexibility that is very hard to achieve with other numerical methods. The Kologorov-Smirnov test shows that the proposed Markov chain transition density converges to the one given by the likelihood expan...

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

    Dönmez, Orhan

    2004-09-01

    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 GRH equations are obtained by high resolution shock Capturing schemes (HRSC), specifically designed to solve nonlinear 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 one, two and three dimensions. 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 GRH equations are tested using two different test problems which are Geodesic flow and Circular motion of particle In order to do this, the flux part of GRH equations is coupled with 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.

  1. Heterogeneity-mediated cellular adaptation and its trade-off: searching for the general principles of diseases.

    Heng, Henry H

    2016-07-15

    Big-data-omics have promised the success of precision medicine. However, most common diseases belong to adaptive systems where the precision is all but difficult to achieve. In this commentary, I propose a heterogeneity-mediated cellular adaptive model to search for the general model of diseases, which also illustrates why in most non-infectious non-Mendelian diseases the involvement of cellular evolution is less predictable when gene profiles are used. This synthesis is based on the following new observations/concepts: 1) the gene only codes "parts inheritance" while the genome codes "system inheritance" or the entire blueprint; 2) the nature of somatic genetic coding is fuzzy rather than precise, and genetic alterations are not just the results of genetic error but are in fact generated from internal adaptive mechanisms in response to environmental dynamics; 3) stress-response is less specific within cellular evolutionary context when compared to known biochemical specificities; and 4) most medical interventions have their unavoidable uncertainties and often can function as negative harmful stresses as trade-offs. The acknowledgment of diseases as adaptive systems calls for the action to integrate genome- (not simply individual gene-) mediated cellular evolution into molecular medicine.

  2. An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

    Shen Yi

    2013-01-01

    In this paper,we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabási-Albert (BA) scale-free networks.This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets.We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes.Furthermore,with the increase of prediction orders,we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

  3. Nested generalized linear mixed model with ordinal response: Simulation and application on poverty data in Java Island

    Widyaningsih, Yekti; Saefuddin, Asep; Notodiputro, Khairil A.; Wigena, Aji H.

    2012-05-01

    The objective of this research is to build a nested generalized linear mixed model using an ordinal response variable with some covariates. There are three main jobs in this paper, i.e. parameters estimation procedure, simulation, and implementation of the model for the real data. At the part of parameters estimation procedure, concepts of threshold, nested random effect, and computational algorithm are described. The simulations data are built for 3 conditions to know the effect of different parameter values of random effect distributions. The last job is the implementation of the model for the data about poverty in 9 districts of Java Island. The districts are Kuningan, Karawang, and Majalengka chose randomly in West Java; Temanggung, Boyolali, and Cilacap from Central Java; and Blitar, Ngawi, and Jember from East Java. The covariates in this model are province, number of bad nutrition cases, number of farmer families, and number of health personnel. In this modeling, all covariates are grouped as ordinal scale. Unit observation in this research is sub-district (kecamatan) nested in district, and districts (kabupaten) are nested in province. For the result of simulation, ARB (Absolute Relative Bias) and RRMSE (Relative Root of mean square errors) scale is used. They show that prov parameters have the highest bias, but more stable RRMSE in all conditions. The simulation design needs to be improved by adding other condition, such as higher correlation between covariates. Furthermore, as the result of the model implementation for the data, only number of farmer family and number of medical personnel have significant contributions to the level of poverty in Central Java and East Java province, and only district 2 (Karawang) of province 1 (West Java) has different random effect from the others. The source of the data is PODES (Potensi Desa) 2008 from BPS (Badan Pusat Statistik).

  4. A second-order sharp numerical method for solving the linear elasticity equations on irregular domains and adaptive grids - Application to shape optimization

    Theillard, Maxime; Djodom, Landry Fokoua; Vié, Jean-Léopold; Gibou, Frédéric

    2013-01-01

    We present a numerical method for solving the equations of linear elasticity on irregular domains in two and three spatial dimensions. We combine a finite volume and a finite difference approaches to derive discretizations that produce second-order accurate solutions in the L∞-norm. Our discretization is 'sharp' in the sense that the physical boundary conditions (mixed Dirichlet/Neumann-type) are imposed at the interface and the solution is computed inside the irregular domain only, without the need of smearing the solution across the interface. The irregular domain is represented implicitly using a level-set function so that this approach is applicable to free moving boundary problems; we provide a simple example of shape optimization to illustrate this capability. In addition, we provide an extension of our method to the case of adaptive meshes in both two and three spatial dimensions: we use non-graded quadtree (2D) and octree (3D) data structures to represent the grid that is automatically refined near the irregular domain's boundary. This extension to quadtree/octree grids produces second-order accurate solutions albeit non-symmetric linear systems, due to the node-based sampling nature of the approach. However, the linear system can be solved with simple linear solvers; in this work we use the BICGSTAB algorithm.

  5. Analysis of Adaptive Feedback and Echo Cancelation Algorithms in A General Multiple-Microphone and Single-Loudspeaker System

    Guo, Meng; Elmedyb, Thomas Bo; Jensen, Søren Holdt;

    2011-01-01

    In this paper, we analyze a general multiple-microphone and single-loudspeaker system, where an adaptive algorithm is used to cancel acoustic feedback/echo and a beamformer processes the feedback/echo canceled signals. This system can be viewed as part of a typical hearing aid system and....../or a traditional acoustic echo cancelation system. We introduce and derive an approximation of a useful frequency domain measure - the power transfer function - and show how to predict the system stability bound, convergence rate and the steady-state behavior across time and frequency. Furthermore, we show how...... the derived expressions can be used to determine e.g. the step size parameter in the adaptive algorithms to achieve a desired system property e.g. convergence rate at a specific frequency....

  6. Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling

    Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik

    2008-01-01

    estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty...

  7. SafeBox: adaptable spatio-temporal generalization for location privacy protection

    Sergio Mascetti

    2014-08-01

    Full Text Available Spatial and temporal generalization emerged in the literature as a common approach to preserve location privacy. However, existing solutions have two main shortcomings. First, spatiotemporal generalization can be used with different objectives: for example, to guarantee anonymity or to decrease the sensitivity of the location information. Hence, the strategy used to compute the generalization can follow different semantics often depending on the privacy threat, while most of the existing solutions are specifically designed for a single semantics. Second, existing techniques prevent the so-called inversion attack by adopting a top-down strategy that needs to acquire a large amount of information. This may not be feasible when this information is dynamic (e.g., position or properties of objects and needs to be acquired from external services (e.g., Google Maps. In this contribution we present a formal model of the problem that is compatible with most of the semantics proposed so far in the literature, and that supports new semantics as well. Our BottomUp algorithm for spatio-temporal generalization is compatible with the use of online services, it supports generalizations based on arbitrary semantics, and it is safe with respect to the inversion attack. By considering two datasets and two examples of semantics, we experimentally compare BottomUp with a more classical top-down algorithm, showing that BottomUp is efficient and guarantees better performance in terms of the average size (space and time of the generalized regions.

  8. [A Brillouin Scattering Spectrum Feature Extraction Based on Flies Optimization Algorithm with Adaptive Mutation and Generalized Regression Neural Network].

    Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

    2015-10-01

    According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.

  9. Accurate and general treatment of electrostatic interaction in Hamiltonian adaptive resolution simulations

    Heidari, M.; Cortes-Huerto, R.; Donadio, D.; Potestio, R.

    2016-10-01

    In adaptive resolution simulations the same system is concurrently modeled with different resolution in different subdomains of the simulation box, thereby enabling an accurate description in a small but relevant region, while the rest is treated with a computationally parsimonious model. In this framework, electrostatic interaction, whose accurate treatment is a crucial aspect in the realistic modeling of soft matter and biological systems, represents a particularly acute problem due to the intrinsic long-range nature of Coulomb potential. In the present work we propose and validate the usage of a short-range modification of Coulomb potential, the Damped shifted force (DSF) model, in the context of the Hamiltonian adaptive resolution simulation (H-AdResS) scheme. This approach, which is here validated on bulk water, ensures a reliable reproduction of the structural and dynamical properties of the liquid, and enables a seamless embedding in the H-AdResS framework. The resulting dual-resolution setup is implemented in the LAMMPS simulation package, and its customized version employed in the present work is made publicly available.

  10. On-line Multiple-model Based Adaptive Control Reconfiguration for a Class of Non-linear Control Systems

    Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.

    2000-01-01

    Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets of ...... corresponding to the updating of local LTI models, which validations are determined by the model approximation errors and the optimal index of local design. The test on a nonlinear ship propulsion system shows the promising potential of this method for system reconfiguration...

  11. On-line Multiple-model Based Adaptive Control Reconfiguration for a Class of Non-linear Control Systems

    Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.

    2000-01-01

    Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets...... of LTI models are employed to approximate the faulty, reconfigured and nominal nonlinear systems respectively with respect to the on-line information of the operating system, and a set of compensating modules are proposed and designed so as to make the local LTI model approximating to the reconfigured...

  12. Adaptive control and synchronization of chaotic systems consisting of Van der Pol oscillators coupled to linear oscillators

    Fotsin, Hilaire [Laboratoire d' Electronique, Departement de Physique, Faculte des Sciences, Universite de Dschang, B.P. 67 Dschang (Cameroon); INPL-CRAN, UMR CNRS-INPL-UHP 7039 ENSEM-2, Avenue de la Foret de Haye-54516, Vandoeuvre-les-Nancy Cedex (France); E-mail: hbfotsin@yahoo.fr; Bowong, Samuel [Laboratoire de Mathematiques Appliquees, Departement de Mathematiques et Informatique, Faculte des sciences, Universite de Douala, B.P. 24157 Douala (Cameroon)] e-mail: sbowong@uycdc.uninet.cm

    2006-02-01

    This paper deals with the problem of control and synchronization of coupled second-order oscillators showing a chaotic behavior. A classical feedback controller is first used to stabilize the system at its equilibrium. An adaptive observer is then designed to synchronize the states of the master and slave oscillators using a single scalar signal corresponding to an observable state variable of the driving oscillator. An interesting feature of the proposed approach is that it can be used for chaos control as well as synchronization purposes. Numerical simulations results confirming the analytical predictions are shown and pspice simulations are also performed to confirm the efficiency of the proposed control scheme.

  13. Direct 4D parametric imaging for linearized models of reversibly binding PET tracers using generalized AB-EM reconstruction

    Rahmim, Arman; Zhou, Yun; Tang, Jing; Lu, Lijun; Sossi, Vesna; Wong, Dean F.

    2012-02-01

    Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains one of most active areas in dynamic brain PET imaging, which in the vast majority of cases involves receptor/transporter studies with reversibly binding tracers. As such, the focus of this work has been to develop a novel direct 4D parametric image reconstruction scheme for such tracers. Based on a relative equilibrium (RE) graphical analysis formulation (Zhou et al 2009b Neuroimage 44 661-70), we developed a closed-form 4D EM algorithm to directly reconstruct distribution volume (DV) parametric images within a plasma input model, as well as DV ratio (DVR) images within a reference tissue model scheme (wherein an initial reconstruction was used to estimate the reference tissue time-activity curves). A particular challenge with the direct 4D EM formulation is that the intercept parameters in graphical (linearized) analysis of reversible tracers (e.g. Logan or RE analysis) are commonly negative (unlike for irreversible tracers, e.g. using Patlak analysis). Subsequently, we focused our attention on the AB-EM algorithm, derived by Byrne (1998, Inverse Problems 14 1455-67) to allow inclusion of prior information about the lower (A) and upper (B) bounds for image values. We then generalized this algorithm to the 4D EM framework, thus allowing negative intercept parameters. Furthermore, our 4D AB-EM algorithm incorporated and emphasized the use of spatially varying lower bounds to achieve enhanced performance. As validation, the means of parameters estimated from 55 human 11C-raclopride dynamic PET studies were used for extensive simulations using a mathematical brain phantom. Images were reconstructed using conventional indirect as well as proposed direct parametric imaging methods. Noise versus bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy

  14. A Comparison between Linear IRT Observed-Score Equating and Levine Observed-Score Equating under the Generalized Kernel Equating Framework

    Chen, Haiwen

    2012-01-01

    In this article, linear item response theory (IRT) observed-score equating is compared under a generalized kernel equating framework with Levine observed-score equating for nonequivalent groups with anchor test design. Interestingly, these two equating methods are closely related despite being based on different methodologies. Specifically, when…

  15. High-fidelity linear time-invariant model of a smart rotor with adaptive trailing edge flaps

    Bergami, Leonardo; Hansen, Morten Hartvig

    2017-01-01

    aero-servo-elastic model support the design, systematic tuning and model synthesis of smart rotor control systems. As an example application, the gains of an individual flap controller are tuned using the Ziegler-Nichols method for the full-order poles. The flap controller is based on feedback...... of inverse Coleman transformed and low-pass filtered flapwise blade root moments to the cyclic flap angles through two proportional-integral controllers. The load alleviation potential of the active flap control, anticipated by the frequency response of the linear closed-loop model, is also confirmed by non...

  16. 非线性自适应拥塞控制算法研究%Study of Non-Linear Adaptive Congestion Control Algorithm

    范训礼; 郑锋; Lin GUAN

    2011-01-01

    研究丢弃概率的变化率与队列长度稳定性间的关系,分析ARED算法及REM算法的丢弃概率计算函数,采用非线性化函数计算丢弃概率,提出一种非线性自适应拥塞控制算法(NLACCA),根据队列长度与目标队列长度中值的偏离程度动态地调整丢弃概率的变化率,从而减小队列长度波动,提高算法稳定性.在NS-2上进行的大量实验结果表明,该算法具有队列长度抖动性小、平均时延低、丢包数少等特点.%This paper studies the relationship between the changing rate of drop probability and the queue stability, and specifically researches computing function of dropping probability of Adaptive Random Early Detection(ARED) algorithm and Random Exponent Marking(REM) algorithm respectively. As a result, a Non-Linear Adaptive Congestion Control Algorithm(NLACCA) is proposed. Based on the Active Queue Management(AQM) scheme, which provides a non-linear adaptation to the dropping probability function of the ARED, NLACCA enables the dropping probability gradient to vary along with the deviation that is between the instantaneous queue length and the target queue length. NLACCA can not only reduce the jitter of the target queue length, but also improve the stability of the algorithm. Simulation results demonstrate that the NLAC CA algorithm outperforms in most scenarios, such as the jitter of queue length, delay, and packets dropped.

  17. Abundance of general aerobic heterotrophic bacteria in the Bering Sea and Chukchi Sea and their adaptation to temperature

    陈皓文; 高爱国; 孙海青; 矫玉田

    2004-01-01

    The abundance of general aerobic heterotrophic bacteria(GAB) from the water and sediment in the Bering Sea and the Chukchi Sea was determined by using petri dish cultivation and counting method. The abundance of GAB among the different sea areas, sampling sites, layers of sediments surveyed and adaptability to differential temperatures was studied. The result obtained showed that: the occurrence percentage of GAB in the surface water was higher than that in sediment, but the abundance was only 0.17% of sediment. The occurrence percentage of GAB in surficial layer of sediment was higher than that in the other layers. The occurrence percentage of GAB in surficial layer of sediment was higher than that in the other layers. The occurrence percentage, abundance and its variation of GAB in the Bering Sea were higher than that in the Chukchi Sea respectively. The average value of the abundance of GAB in sediment showed a trend: roughly higher in the lower latitudinal area than higher latitude. The results from temperature test mean that: the majority of bacteria tested were cold -adapted ones, minority might be mesophilic bacteria. The results indicated that, Arctic ocean bacteria had a stronger adaptability to environmental temperature.

  18. Adaptive Generalized Projective Synchronization of Takagi-Sugeno Fuzzy Drive-response Dynamical Networks with Time Delay

    ZHENG Yong-Ai

    2012-01-01

    Time-delay Takagi-Sugeno fuzzy drive-response dynamical networks (TD-TSFDRDNs) are defined by extending the drive-response dynamical networks. Based on the LaSalle invariant principle, a simple and systematic adaptive control scheme is proposed to synchronize the TD-TSFDRDNs with a desired scalar factor. A sufficient condition for the generalized projective synchronization in TD-TSFDRDNs is derived. Moreover, numerical simulations are provided to verify the correctness and effectiveness of the scheme.%Time-delay Takagi-Sugeno fuzzy drive-response dynamical networks (TD-TSFDRDNs) are defined by extending the drive-response dynamical networks.Based on the LaSalle invariant principle,a simple and systematic adaptive control scheme is proposed to synchronize the TD-TSFDRDNs with a desired scalar factor.A sufficient condition for the generalized projective synchronization in TD-TSFDRDNs is derived.Moreover,numerical simulations are provided to verify the correctness and effectiveness of the scheme.

  19. When is it adaptive to be patient? A general framework for evaluating delayed rewards.

    Fawcett, Tim W; McNamara, John M; Houston, Alasdair I

    2012-02-01

    The tendency of animals to seek instant gratification instead of waiting for greater long-term benefits has been described as impatient, impulsive or lacking in self-control. How can we explain the evolution of such seemingly irrational behaviour? Here we analyse optimal behaviour in a variety of simple choice situations involving delayed rewards. We show that preferences for more immediate rewards should depend on a variety of factors, including whether the choice is a one-off or is likely to be repeated, the information the animal has about the continuing availability of the rewards and the opportunity to gain rewards through alternative activities. In contrast to the common assertion that rational animals should devalue delayed rewards exponentially, we find that this pattern of discounting is optimal only under restricted circumstances. We predict preference reversal whenever waiting for delayed rewards entails loss of opportunities elsewhere, but the direction of this reversal depends on whether the animal will face the same choice repeatedly. Finally, we question the ecological relevance of standard laboratory tests for impulsive behaviour, arguing that animals rarely face situations analogous to the self-control paradigm in their natural environment. To understand the evolution of impulsiveness, a more promising strategy would be to identify decision rules that are adaptive in a realistic ecological setting, and examine how these rules determine patterns of behaviour in simultaneous choice tests.

  20. Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

    Futao Guo; Guangyu Wang; John L Innes; Xiangqing Ma; Long Sun; Haiqing Hu

    2015-01-01

    The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing’an Mountains, in northeast China. The response variables were the area burned by lightning-caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log-linear model (log-transformation on both response and predictor variables), and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regression model and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at a=0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela-tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.

  1. The linearization with generalized F(t) exponential dichotomy%广义F(t)指数型二分性条件下的线性化

    高永飞

    2012-01-01

    In this paper, we extend Hartman's linearization theorem and Palmer's linearization theorem to the system with generalized F(t) exponential dichotomy, and some suitable assumptions are added, then system x = A (t)x +f ( t, x ) is topologically equivalent to x = A (t) x.%将Hartman线性化定理Palmer线性化定理推广到具有广义F(t)指数型二分性的系统,在一定条件下,我们可以得到系统=A(t)x+f(t,x)拓扑等价于其线性部分=A(t)x.

  2. Diffractive generalized phase contrast for adaptive phase imaging and optical security

    Palima, Darwin; Glückstad, Jesper

    2012-01-01

    We analyze the properties of Generalized Phase Contrast (GPC) when the input phase modulation is implemented using diffractive gratings. In GPC applications for patterned illumination, the use of a dynamic diffractive optical element for encoding the GPC input phase allows for onthe- fly...... optimization of the input aperture parameters according to desired output characteristics. For wavefront sensing, the achieved aperture control opens a new degree of freedom for improving the accuracy of quantitative phase imaging. Diffractive GPC input modulation also fits well with grating-based optical...

  3. A new highly adaptable design of shear-flow device for orientation of macromolecules for Linear Dichroism (LD) measurement

    Lundahl, P. Johan

    2011-01-01

    This article presents a new design of flow-orientation device for the study of bio-macromolecules, including DNA and protein complexes, as well as aggregates such as amyloid fibrils and liposome membranes, using Linear Dichroism (LD) spectroscopy. The design provides a number of technical advantages that should make the device inexpensive to manufacture, easier to use and more reliable than existing techniques. The degree of orientation achieved is of the same order of magnitude as that of the commonly used concentric cylinders Couette flow cell, however, since the device exploits a set of flat strain-free quartz plates, a number of problems associated with refraction and birefringence of light are eliminated, increasing the sensitivity and accuracy of measurement. The device provides similar shear rates to those of the Couette cell but is superior in that the shear rate is constant across the gap. Other major advantages of the design is the possibility to change parts and vary sample volume and path length easily and at a low cost. © 2011 The Royal Society of Chemistry.

  4. Towards a Robust Solution of the Non-linear Kinematics for the General Stewart Platform with Estimation of Distribution Algorithms

    Eusebio Eduardo Hernández Martinez

    2013-01-01

    Full Text Available In robotics, solving the direct kinematics problem (DKP for parallel robots is very often more difficult and time consuming than for their serial counterparts. The problem is stated as follows: given the joint variables, the Cartesian variables should be computed, namely the pose of the mobile platform. Most of the time, the DKP requires solving a non‐linear system of equations. In addition, given that the system could be non‐convex, Newton or Quasi‐Newton (Dogleg based solvers get trapped on local minima. The capacity of such kinds of solvers to find an adequate solution strongly depends on the starting point. A well‐known problem is the selection of such a starting point, which requires a priori information about the neighbouring region of the solution. In order to circumvent this issue, this article proposes an efficient method to select and to generate the starting point based on probabilistic learning. Experiments and discussion are presented to show the method performance. The method successfully avoids getting trapped on local minima without the need for human intervention, which increases its robustness when compared with a single Dogleg approach. This proposal can be extended to other structures, to any non‐linear system of equations, and of course, to non‐linear optimization problems.

  5. Cultural adaptation into Spanish of the generalized anxiety disorder-7 (GAD-7 scale as a screening tool

    Pérez-Páramo María

    2010-01-01

    Full Text Available Abstract Background Generalized anxiety disorder (GAD is a prevalent mental health condition which is underestimated worldwide. This study carried out the cultural adaptation into Spanish of the 7-item self-administered GAD-7 scale, which is used to identify probable patients with GAD. Methods The adaptation was performed by an expert panel using a conceptual equivalence process, including forward and backward translations in duplicate. Content validity was assessed by interrater agreement. Criteria validity was explored using ROC curve analysis, and sensitivity, specificity, predictive positive value and negative value for different cut-off values were determined. Concurrent validity was also explored using the HAM-A, HADS, and WHO-DAS-II scales. Results The study sample consisted of 212 subjects (106 patients with GAD with a mean age of 50.38 years (SD = 16.76. Average completion time was 2'30''. No items of the scale were left blank. Floor and ceiling effects were negligible. No patients with GAD had to be assisted to fill in the questionnaire. The scale was shown to be one-dimensional through factor analysis (explained variance = 72%. A cut-off point of 10 showed adequate values of sensitivity (86.8% and specificity (93.4%, with AUC being statistically significant [AUC = 0.957-0.985; p 0.001. Limitations Elderly people, particularly those very old, may need some help to complete the scale. Conclusion After the cultural adaptation process, a Spanish version of the GAD-7 scale was obtained. The validity of its content and the relevance and adequacy of items in the Spanish cultural context were confirmed.

  6. Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

    Bagherpoor, H M; Salmasi, Farzad R

    2015-07-01

    In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance.

  7. Time Frame and Justice Motive: Future Perspective Moderates the Adaptive Function of General Belief in a Just World

    Wu, Michael Shengtao; Sutton, Robbie M.; Yan, Xiaodan; Zhou, Chan; Chen, Yiwen; Zhu, Zhuohong; Han, Buxin

    2013-01-01

    Background The human ability to envision the future, that is, to take a future perspective (FP), plays a key role in the justice motive and its function in transcending disadvantages and misfortunes. The present research investigated whether individual (Study 1) and situational (Study 2) differences in FP moderated the association of general belief in a just world (GBJW) with psychological resilience. Methodology/Principal Findings We investigated FP, GBJW, and resilience in sample of adolescents (n = 223) and disaster survivors (n = 218) in China. In Study 1, adolescents revealed stronger GBJW than PBJW, and GBJW uniquely predicted resilience in the daily lives of those with high FP (but not those with low FP). In Study 2, natural priming of FP (vs. no FP) facilitated the association of GBJW with resilience after disaster. Conclusions/Significance Supporting predictions, participants endorsed GBJW more strongly than PBJW. Further, GBJW interacted with FP in both studies, such that there was an association between GBJW and resilience at high but not low levels of FP. The results corroborate recent findings suggesting that GBJW may be more psychologically adaptive than PBJW among some populations. They also confirm that focusing on the future is an important aspect of the adaptive function of just-world beliefs. PMID:24312235

  8. A comparison of spike time prediction and receptive field mapping with point process generalized linear models, Wiener-Voltera kernels, and spike-triggered averaging methods

    Chen Zhe; Neuenschwander Sergio; Lima Bruss; Pipa Gordon; Brown Emery N

    2009-01-01

    Poster presentation: Characterizing neuronal encoding is essential for understanding information processing in the brain. Three methods are commonly used to characterize the relationship between neural spiking activity and the features of putative stimuli. These methods include: Wiener-Volterra kernel methods (WVK), the spike-triggered average (STA), and more recently, the point process generalized linear model (GLM). We compared the performance of these three approaches in estimating recepti...

  9. 广义线性热弹性力学模型的唯一性%Uniqueness for the Linear Theory of Generalized Thermo-Elasticity

    郭兴明

    2001-01-01

    In this paper, the uniqueness for the linear theory of a new generalized thermo-elastic model of the continuum with the centre symmetry is shown under less assumptions, whose constitutive equations contain deformation, temperature and its rate as well as its gradient, electric field and its gradient. So the phase variation is pemitted when the deformation proceeds as long as the constitutive equations preserve their original forms.

  10. 广义线性热弹性力学模型的唯一性%Uniqueness for the Linear Theory of Generalized Thermo-Elasticity

    郭兴明

    2000-01-01

    In this paper, the uniqueness for the linear theory of a new generalized thermo-elastic model of the continuum with the centre symmetry is shown under less assumptions, whose constitutive equations contain deformation, temperature and its rate as well as its gradient, electric field and its gradient. So the phase variation is pemitted when the deformation proceeds as long as the constitutive equations preserve their original forms.

  11. A dynamically bi-orthogonal method for time-dependent stochastic partial differential equations II: Adaptivity and generalizations

    Cheng, Mulin, E-mail: mulinch@caltech.edu [Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 (United States); Hou, Thomas Y., E-mail: hou@cms.caltech.edu [Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 (United States); Zhang, Zhiwen, E-mail: zhangzw@caltech.edu [Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 (United States)

    2013-06-01

    This is part II of our paper in which we propose and develop a dynamically bi-orthogonal method (DyBO) to study a class of time-dependent stochastic partial differential equations (SPDEs) whose solutions enjoy a low-dimensional structure. In part I of our paper [9], we derived the DyBO formulation and proposed numerical algorithms based on this formulation. Some important theoretical results regarding consistency and bi-orthogonality preservation were also established in the first part along with a range of numerical examples to illustrate the effectiveness of the DyBO method. In this paper, we focus on the computational complexity analysis and develop an effective adaptivity strategy to add or remove modes dynamically. Our complexity analysis shows that the ratio of computational complexities between the DyBO method and a generalized polynomial chaos method (gPC) is roughly of order O((m/N{sub p}){sup 3}) for a quadratic nonlinear SPDE, where m is the number of mode pairs used in the DyBO method and N{sub p} is the number of elements in the polynomial basis in gPC. The effective dimensions of the stochastic solutions have been found to be small in many applications, so we can expect m is much smaller than N{sub p} and computational savings of our DyBO method against gPC are dramatic. The adaptive strategy plays an essential role for the DyBO method to be effective in solving some challenging problems. Another important contribution of this paper is the generalization of the DyBO formulation for a system of time-dependent SPDEs. Several numerical examples are provided to demonstrate the effectiveness of our method, including the Navier–Stokes equations and the Boussinesq approximation with Brownian forcing.

  12. Research on linear adaptive disturbance rejection control method for yaw tracking of unmanned rotorcraft%无人旋翼机线性自抗扰航向控制

    彭艳; 刘梅; 罗均; 谢少荣

    2013-01-01

    研究无人旋翼机器人在干扰情况下的航向控制问题.无人旋翼机航向动力学包含输入非线性、时变参数和主-尾旋翼之间的强耦合,难以建立精确的数学模型,并且易受外部扰动影响,很难达到良好的控制性能.针对这一问题提出基于线性自抗扰控制(linear adaptive disturbance rejection control,LADRC)的航向控制方法,通过设计扩张线性状态观测器对未知模型和外界干扰进行实时估计并进行在线补偿.以自主研制的无人旋翼机为例,建立其航向动力学方程,把通道间的交叉耦合影响视为不确定扰动,将其与外部干扰作为扩张状态,利用观测器带宽确定观测器增益,设计线性扩张状态观测器来跟踪各阶扩张状态变量,为说明LADRC的有效性,选用PD控制为非线性状态误差反馈控制律实现航向控制.仿真以及试验结果表明在外部扰动或模型结构参数发生变化时控制器仍可获得理想的动态性能,具有很好的适应性和鲁棒性.%The yaw tracking problem of unmanned rotorcraft under disturbance condition is studied.The yaw dynamics of unmanned rotorcraft involves input nonlinearity,time-varying parameters and the strong coupling between main and tail rotors,which is difficult to establish an accurate mathematic model and vulnerable to external disturbance.All of these make it difficult to have a good tracking performance.Aiming at these problems,a yaw tracking control method based on linear adaptive disturbance rejection control (LADRC) is presented.The linear extended state observer (LESO) is designed to implement the real-time estimation and online compensation of the unknown model and external disturbance.The self-made unmanned rotorcraft is studied using this method;The yaw dynamics model is built up;then the cross coupling effect between channels is taken as the uncertain disturbance,which is combined with the external disturbance and both of them are taken as the

  13. Generalized two-dimensional (2D) linear system analysis metrics (GMTF, GDQE) for digital radiography systems including the effect of focal spot, magnification, scatter, and detector characteristics

    Kuhls-Gilcrist, Andrew T.; Gupta, Sandesh K.; Bednarek, Daniel R.; Rudin, Stephen

    2010-01-01

    The MTF, NNPS, and DQE are standard linear system metrics used to characterize intrinsic detector performance. To evaluate total system performance for actual clinical conditions, generalized linear system metrics (GMTF, GNNPS and GDQE) that include the effect of the focal spot distribution, scattered radiation, and geometric unsharpness are more meaningful and appropriate. In this study, a two-dimensional (2D) generalized linear system analysis was carried out for a standard flat panel detector (FPD) (194-micron pixel pitch and 600-micron thick CsI) and a newly-developed, high-resolution, micro-angiographic fluoroscope (MAF) (35-micron pixel pitch and 300-micron thick CsI). Realistic clinical parameters and x-ray spectra were used. The 2D detector MTFs were calculated using the new Noise Response method and slanted edge method and 2D focal spot distribution measurements were done using a pin-hole assembly. The scatter fraction, generated for a uniform head equivalent phantom, was measured and the scatter MTF was simulated with a theoretical model. Different magnifications and scatter fractions were used to estimate the 2D GMTF, GNNPS and GDQE for both detectors. Results show spatial non-isotropy for the 2D generalized metrics which provide a quantitative description of the performance of the complete imaging system for both detectors. This generalized analysis demonstrated that the MAF and FPD have similar capabilities at lower spatial frequencies, but that the MAF has superior performance over the FPD at higher frequencies even when considering focal spot blurring and scatter. This 2D generalized performance analysis is a valuable tool to evaluate total system capabilities and to enable optimized design for specific imaging tasks. PMID:21243038

  14. Adapt or Die

    Brody, Joshua Eric; Larsen, Kasper Green

    2015-01-01

    read cells. We study such non-adaptive data structures in the cell probe model. This model is one of the least restrictive lower bound models and in particular, cell probe lower bounds apply to data structures developed in the popular word-RAM model. Unfortunately, this generality comes at a high cost...... several different notions of non-adaptivity and identify key properties that must be dealt with if we are to prove polynomial lower bounds without restrictions on the data structures. Finally, our results also unveil an interesting connection between data structures and depth-2 circuits. This allows us...... to translate conjectured hard data structure problems into good candidates for high circuit lower bounds; in particular, in the area of linear circuits for linear operators. Building on lower bound proofs for data structures in slightly more restrictive models, we also present a number of properties of linear...

  15. On the Linear Regime of the Characteristic formulation of General relativity in the Minkowski and Schwarzschild's Backgrounds

    Montaña, Carlos Eduardo Cedeño

    2016-01-01

    We present here the linear regime of the Einstein's field equations in the characteristic formulation. Through a simple decomposition of the metric variables in spin-weighted spherical harmonics, the field equations are expressed as a system of coupled ordinary differential equations. The process for decoupling them leads to a simple equation for $J$ - one of the Bondi-Sachs metric variables - known in the literature as the master equation. Then, this last equation is solved in terms of Bessel's functions of the first kind for the Minkowski's background, and in terms of the Heun's function in the Schwarzschild's case. In addition, when a matter source is considered, the boundary conditions across the time-like world tubes bounding the source are taken into account. These boundary conditions are computed for all multipole modes. Some examples as the point particle binaries in circular and eccentric orbits, in the Minkowski's background are shown as particular cases of this formalism.

  16. GENERALIZED PRECONDITIONED HERMITIAN AND SKEW-HERMITIAN SPLITTING METHODS FOR NON-HERMITIAN POSITIVE-DEFINITE LINEAR SYSTEMS

    Junfeng Yin; Quanyu Dou

    2012-01-01

    In this paper,a generalized preconditioned Hermitian and skew-Hermitian splitting (GPHSS) iteration method for a non-Hermitian positive-definite matrix is studied,which covers standard Hermitian and skew-Hermitian splitting (HSS) iteration and also many existing variants.Theoretical analysis gives an upper bound for the spectral radius of the iteration matrix.From practical point of view,we have analyzed and implemented inexact generalized preconditioned Hermitian and skew-Hermitian splitting (IGPHSS) iteration,which employs Krylov subspace methods as its inner processes.Numerical experiments from three-dimensional convection-diffusion equation show that the GPHSS and IGPHSS iterations are efficient and competitive with standard HSS iteration and AHSS iteration.

  17. Climateric: fatigue or third stage of the general adaptation syndrome Climaterio: fatiga o tercera etapa del síndrome de adaptación general

    William Alvarez Gaviria

    2004-09-01

    Full Text Available The origin of climacteric has been subject of debate. Most opinions agree in that it arises exclusively from natural selection. In this paper the author argues that, besides this reason there is another, even more important; for him, climacteric is the final response to fatigue or the third stage of the general adaptation syndrome, just as in elderly people there is a loss of the capacity of proliferation of fibroblasts and lack of response to insulin. From a genetic point of view, this corresponds to an antagonic pleiotropy: the genetic program that has made the human adrenergic and corticotropic systems hyperactive, has also caused that they do not reach senescence intact. High concentrations of stress hormones during youth and adulthood in humans, as compared to chimpanzees, gorillas and orangutans, and the hormonal cascade reactions elicited by them are meaningfully related to our most conspicuous illnesses, our genotype/phenotype and, in the long term, with climacteric. Se ha conjeturado a menudo sobre las razones del climaterio y la mayoría de los autores sostiene que es un fenómeno que surge exclusivamente de la selección natural. Aquí asumimos que, aunque esa sea parte de la explicación, no es la razón primordial. Así como con la edad se da la pérdida, por ejemplo, de la capacidad proliferativa de los fibroblastos y de la sensibilidad a la insulina, el climaterio podría corresponder no más que a la fatiga o tercera etapa del Síndrome de Adaptación General. En un enfoque genético correspondería, pues, a una pleiotropía antagónica: el programa genético que ha hecho hiperactivos a los sistemas adrenérgico y corticotrópico del ser humano, evitaría también que llegara incólume al punto final de senescencia. Las altas concentraciones de hormonas de estrés en la juventud y la edad adulta que distinguen a nuestra especie, comparada con el chimpancé, el gorila y el orangután, y las reacciones hormonales en cascada que

  18. Polynomial approximation of functions of matrices and its application to the solution of a general system of linear equations

    Tal-Ezer, Hillel

    1987-01-01

    During the process of solving a mathematical model numerically, there is often a need to operate on a vector v by an operator which can be expressed as f(A) while A is NxN matrix (ex: exp(A), sin(A), A sup -1). Except for very simple matrices, it is impractical to construct the matrix f(A) explicitly. Usually an approximation to it is used. In the present research, an algorithm is developed which uses a polynomial approximation to f(A). It is reduced to a problem of approximating f(z) by a polynomial in z while z belongs to the domain D in the complex plane which includes all the eigenvalues of A. This problem of approximation is approached by interpolating the function f(z) in a certain set of points which is known to have some maximal properties. The approximation thus achieved is almost best. Implementing the algorithm to some practical problem is described. Since a solution to a linear system Ax = b is x= A sup -1 b, an iterative solution to it can be regarded as a polynomial approximation to f(A) = A sup -1. Implementing the algorithm in this case is also described.

  19. On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs

    Amini, Nina H. [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States); CNRS, Laboratoire des Signaux et Systemes (L2S) CentraleSupelec, Gif-sur-Yvette (France); Miao, Zibo; Pan, Yu; James, Matthew R. [Australian National University, ARC Centre for Quantum Computation and Communication Technology, Research School of Engineering, Canberra, ACT (Australia); Mabuchi, Hideo [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States)

    2015-12-15

    The purpose of this paper is to study the problem of generalizing the Belavkin-Kalman filter to the case where the classical measurement signal is replaced by a fully quantum non-commutative output signal. We formulate a least mean squares estimation problem that involves a non-commutative system as the filter processing the non-commutative output signal. We solve this estimation problem within the framework of non-commutative probability. Also, we find the necessary and sufficient conditions which make these non-commutative estimators physically realizable. These conditions are restrictive in practice. (orig.)

  20. Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

    Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

    2014-01-01

    Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.