State controllability computation technique for linear time‐varying systems by using Taylor series approximation

This article presents a technique to determine the controllability Grammian matrix (CGM) for linear time‐varying systems by using truncated Taylor polynomial vector and the operational matrix of integration. An important property of this algorithm is that it starts by integrating the Lyapunov differential matrix equation in terms of the CGM. However, the algorithm does not use the mathematical integration processes actually, but uses the truncated Taylor polynomial vector and the operational matrix of integration. Thus, the problem is reduced to solving a linear set of algebraic equations with constant coefficients consisting of the Taylor polynomial constant coefficients of each of the CGM elements. Numerical results and error curves are given to illustrate the improvements achieved by the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Modeling Federal Funds rates: a comparison of four methodologies

Monthly Federal Fund interest rate values, set by the Federal Open Market Committee, have been the subject of much speculation prior to the announcement of their new values each period. In this study we use four competing methodologies to model and forecast the behavior of these short term Federal Fund interest rates. These methodologies are: time series, Taylor, econometric and neural network. The time series forecasts use only past values of Federal Funds rates. The celebrated Taylor rule methodology theorizes that the Federal Fund rate values are influenced solely by deviations from a desired level of inflation and from potential output. The econometric and neural network models have inputs used by both the time series and Taylor rule. Using monthly data from 1958 to the end of 2005 we distinguish between sample and out-of-sample sets to train, evaluate, and compare the models’ effectiveness. Our results indicate that the econometric modeling performs better than the other approaches when the data are divided into two sets of pre-Greenspan and Greenspan periods. However, when the data sample is divided into three groups of low, medium and high Federal Funds, the neural network approach does best. An earlier version was presented at the 2007 International Joint Conference on Neural Networks in Orlando. We are thankful to the Larry Medsker, regional editor of Neural Computing and Applications, and to two anonymous referees for very insightful comments that helped us improve the final version.     

An Auto-Adaptive Multidomain Spectral Technique for Linear Stability Analysis: Application to Viscous Compressible Flows

An auto-adaptive multidomain pseudo-spectral technique is considered in order to solve the linear stability problem of viscous compressible flows. Both the locations of the interfaces and the parameters of the mappings in each subdomain are adapted by minimizing the H ² -norm of the calculated solution. Such method provides automatically—this is the key point—the best polynomial interpolation of the basic state the stability of which is studied. It turns out that the whole procedure is needed to obtain reliable results. The method is first validated against results available in the literature (both viscous incompressible and inviscid compressible Rayleigh–Taylor configurations). The efficiency of the numerical method is illustrated with results on the linear stability of the compressible viscous diffusive Rayleigh–Taylor flow where no analytical or numerical results are available. New results showing the influence of stratification, viscosity, diffusity between species and thermal diffusivity are presented.     

椭圆口径天线的一种新的设计方法

利用口径坐标变换，比较了圆口径和椭圆口径天线方向函数的差异，得出了将圆口径分布方向图特性映射到椭圆口径的条件，并将圆口径天线最佳综合方法圆Taylor综合应用于椭圆口径天线的设计。将这一条件应用于椭圆离散阵设计，给出了设计实例，比较了连续圆口径分布与椭圆离散阵的特性，扩展了椭圆阵列的常规设计方法。     

Central Bank Learning and Taylor Rules with Sticky Import Prices

This paper examines the outcomes of alternative policy targets for monetary policy in an open economy subject to terms of trade shocks and with less-than-perfect exchange-rate pass through. The central bank also learns the evolution of inflation and output growth in the design of its policy rules. We show that a Taylor rule for the interest rate, based on inflation and output gap targets, when terms of trade shocks are positive, yields more beneficial welfare distributions than comparable feedback rules based only on an inflation target. JEL classification: E5, F4     

The conversion of a dynamic B-spline curve into piecewise polynomials in power form

The evaluation of points and the computations of inflection points or cusps on a curve are often necessary in CAGD applications. When a curve is represented in a B-spline form, such computations can be made easier once it is transformed into a set of piecewise polynomial curves in power form. The usual practice of the transformation of a B-spline curve into a set of piecewise polynomial curves in power form is done either by a knot refinement followed by basis conversions, or by applying a Taylor expansion on each knot span of a B-spline curve.Presented in this paper is a new algorithm to convert a B-spline curve into a set of piecewise polynomial curves in power form. Experiment shows that the proposed algorithm significantly outperforms the conventional approach when one or more control points of a B-spline curve are continuously moving.     

The Pathology of Cryptology—A Current Survey

Abstract

“The Pathology of Cryptology” is the title of a chapter in David Kahn's book The Codebreakers. This chapter mainly deals with hidden messages, which amateur scientists claim to have discovered in the works of William Shakespeare and in other literary works. The present article picks up this subject and provides it with a theoretic framework under the name of “para-steganography.” Moreover, further case studies are added to those given by Kahn, including many examples that only became known after the publication of The Codebreakers. These considerations show that a hidden code can be found almost anywhere, provided that one looks for it in a suitable manner.     

Task-space control of robots using an adaptive Taylor series uncertainty estimator

An uncertainty estimation and compensation can improve the performance of control systems due to structured and unstructured uncertainty. This paper presents a robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators. It is worth noting that not only the lumped uncertainty is estimated and employed in the indirect form of robust controller, but also the upper bound of approximation error is estimated to form a robustifying term and the asymptotic convergence of tracking error and its time derivative are proven based on stability analysis. Finally, the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm (SCARA) robot manipulator.     

基于低阶近似分布源的目标方位估计

广义阵列流型描述的分布源模型,利用了一阶Taylor级数展开,使得模型与分布源空间能量分布形式无关,能够更加广泛地在实际中得到应用,称之为一阶近似分布源模型。研究表明,此种模型忽略高次Taylor级数项后带来的模型误差以及后续方位估计算法的性能损失,因此提出了二阶近似分布源模型,即利用二阶Taylor级数展开。新模型能够进一步减小模型误差,并且对相应的方位估计算法带来较大的性能提升。将分布源的一阶近似模型和二阶近似模型统称为低阶近似分布源模型。随后分析了低阶近似分布源模型在方位估计中的应用,提出了广义SMVDR算法。通过计算机仿真,验证了低阶近似分布源模型方位估计算法的性能,并且研究了分布源的低阶近似模型、空间频率模型和低阶Jacobi-Anger(JA)级数展开模型的模型误差。