The Inverse Problem of Analytic Interpolation With Degree Constraint and Weight Selection for Control Synthesis

The minimizers of certain weighted entropy functionals are the solutions to an analytic interpolation problem with a degree constraint, and all solutions to this interpolation problem arise in this way by a suitable choice of weights. Selecting appropriate weights is pertinent to feedback control synthesis, where interpolants represent closed-loop transfer functions. In this paper we consider the correspondence between weights and interpolants in order to systematize feedback control synthesis with a constraint on the degree. There are two basic issues that we address: we first characterize admissible shapes of minimizers by studying the corresponding inverse problem, and then we develop effective ways of shaping minimizers via suitable choices of weights. This leads to a new procedure for feedback control synthesis.      

A recursive method for solving unconstrained tangentialinterpolation problems

An efficient recursive solution is presented for the one-sided unconstrained tangential interpolation problem. The method relies on the triangular factorization of a certain structured matrix that is implicitly defined by the interpolation data. The recursive procedure admits a physical interpretation in terms of discretized transmission lines. In this framework the generating system is constructed as a cascade of first-order sections. Singular steps occur only when the input data is contradictory, i.e., only when the interpolation problem does not have a solution. Various pivoting schemes can be used to improve numerical accuracy or to impose additional constraints on the interpolants. The algorithm also provides coprime factorizations for all rational interpolants and can be used to solve polynomial interpolation problems such as the general Hermite matrix interpolation problem. A recursive method is proposed to compute a column-reduced generating system that can be used to solve the minimal tangential interpolation problem     

Stability-Preserving Rational Approximation Subject to Interpolation Constraints

A quite comprehensive theory of analytic interpolation with degree constraint, dealing with rational analytic interpolants with an a priori bound, has been developed in recent years. In this paper, we consider the limit case when this bound is removed, and only stable interpolants with a prescribed maximum degree are sought. This leads to weighted $H_2$ minimization, where the interpolants are parameterized by the weights. The inverse problem of determining the weight given a desired interpolant profile is considered, and a rational approximation procedure based on the theory is proposed. This provides a tool for tuning the solution to specifications. The basic idea could also be applied to the case with bounded analytic interpolants.      

A new parameterization of stable polynomials

In this note, we develop a new characterization of stable polynomials. Specifically, given n positive, ordered numbers (frequencies), we develop a procedure for constructing a stable degree n monic polynomial with real coefficients. This construction can be viewed as a mapping from the space of n ordered frequencies to the space of stable degree n monic polynomials. The mapping is one-one and onto, thereby giving a complete parameterization of all stable, degree n monic polynomials. We show how the result can be used to generate parameterizations of stabilizing fixed-order proper controllers for unity feedback systems. We apply these results in the development of stability margin lower bounds for systems with parameter uncertainty.     

A program for the optimum approximation of real rational functions via linear programming

This paper presents a Turbo-Basic program that implements an algorithm for the optimum approximation of real rational functions via linear-programming. The formulation of the linear problem is based on the minimization of a minimax criterion, while its solution is derived through the dual problem. This algorithm is much faster and requires less storage than other approximation techniques. The program is implemented on an IBM-PC AT and tested by several examples. Analytical examples are presented to illustrate how the program is used and the effectiveness of the algorithm.     

Weight Selection in Feedback Design With Degree Constraints

We present an approach for feedback design which is based on recent developments in analytic interpolation with a degree constraint. Performance is cast as an interpolation problem with bounded analytic functions. Minimizers of a certain weighted-entropy functional provide interpolants having degree less than the number of constraints. The choice of weight parameterizes all such bounded degree solutions. However, the relationship between the weights and the shape of corresponding transfer functions is not direct. Thus, in this paper we develop a formalism that guides weight selection.     

Positive interpolation with rational quadratic splines

A necessary and sufficient criterion is presented under which the property of positivity carry over from the data set to rational quadratic spline interpolants. The criterion can always be satisfied if the occuring parameters are properly chosen.     

Matrix-valued Nevanlinna-Pick interpolation with complexity constraint: an optimization approach

Over the last several years, a new theory of Nevanlinna-Pick interpolation with complexity constraint has been developed for scalar interpolants. In this paper we generalize this theory to the matrix-valued case, also allowing for multiple interpolation points. We parameterize a class of interpolants consisting of "most interpolants" of no higher degree than the central solution in terms of spectral zeros. This is a complete parameterization, and for each choice of interpolant we provide a convex optimization problem for determining it. This is derived in the context of duality theory of mathematical programming. To solve the convex optimization problem, we employ a homotopy continuation technique previously developed for the scalar case. These results can be applied to many classes of engineering problems, and, to illustrate this, we provide some examples. In particular, we apply our method to a benchmark problem in multivariate robust control. By constructing a controller satisfying all design specifications but having only half the McMillan degree of conventional H/sup /spl infin// controllers, we demonstrate the advantage of the proposed method.     

Computing Contour Trees for 2D Piecewise Polynomial Functions

Contour trees are extensively used in scalar field analysis. The contour tree is a data structure that tracks the evolution of level set topology in a scalar field. Scalar fields are typically available as samples at vertices of a mesh and are linearly interpolated within each cell of the mesh. A more suitable way of representing scalar fields, especially when a smoother function needs to be modeled, is via higher order interpolants. We propose an algorithm to compute the contour tree for such functions. The algorithm computes a local structure by connecting critical points using a numerically stable monotone path tracing procedure. Such structures are computed for each cell and are stitched together to obtain the contour tree of the function. The algorithm is scalable to higher degree interpolants whereas previous methods were restricted to quadratic or linear interpolants. The algorithm is intrinsically parallelizable and has potential applications to isosurface extraction.     

树状小波局部对比度与图像融合及性能评估

针对红外热成像中目标识别和跟踪的特点,提出了一种基于树状小波变换的局部对比度融合算法,首先采用多尺度树状小波变换的方法对已配准的源图像在相应的能量准则下进行分解,克服了小波变换的移变性;对于分解后的低频子图像采用加权平均的融合规则,对于高频子图像采用局部对比度量测的融合规则,融合图像既保持了源图像的细节信息,又滤去了红外成像中的各类噪声。采用交叉分辨力评价算子量测红外图像中目标与背景的衬比度;通过仿真及基于客观的图像融合评价标准,分别从信息熵、标准差、平均梯度和交叉熵四个参数对图像融合效果进行评估,证明了对于双波段红外辐射图像的融合,提出的融合算法优于小波融合算法、形态学区域分割融合算法。算法尤其适用于红外热成像系统的目标识别和跟踪。     

具有承袭性的切触有理插值算法

有理插值是函数逼近的一个重要内容,而降低切触有理插值的次数和解决切触有理插值函数的存在性是有理插值的一个重要问题。切触有理插值函数的算法大都是基于连分式进行的,其算法可行性是有条件的,且计算量较大。利用牛顿多项式插值承袭性的思想和分段组合的方法,构造出了一种无极点的切触有理插值函数,并推广到向量值切触有理插值情形;既解决了此类切触有理插值函数存在性问题,又降低了切触有理插值函数的次数。给出误差估计,并通过数值实例说明该算法具有承袭性、计算量低、便于实际应用等特点。     

Ratioanl biquadraicC 1-splines inS-convex interpolation

The purpose of this paper is to give a necessary and sufficient condition for theS-convexity of rational biquadraticC ¹-spline interpolants on rectangular grids. The criterion is described in form of linear equalities and convex inequalities, and forS-convex data sets the criterion can be satisfied whenever the rationality parameters are sufficiently large.     

Hermite interpolation: The barycentric approach

The barycentric formulas for polynomial and rational Hermite interpolation are derived; an efficient algorithm for the computation of these interpolants is developed. Some new interpolation principles based on rational interpolation are discussed.     

State space realizations of rational interpolants with prescribed poles

We give a generic algorithm for computing rational interpolants with prescribed poles. The resulting rational function is expressed in the so-called Newton form. State space realizations for this expression of rational functions are given. Our main tool for finding state space realizations is Fuhrmann's shift realization theory from which we obtain concrete realizations by introducing suitable bases of the state space and expressing the abstract operators with respect to these bases in matrix form.     

Identification of spatial PH quintic Hermite interpolants with near-optimal shape measures

The problem of specifying the two free parameters that arise in spatial Pythagorean-hodograph (PH) quintic interpolants to given first-order Hermite data is addressed. Conditions on the data that identify when the “ordinary” cubic interpolant becomes a PH curve are formulated, since it is desired that the selection procedure should reproduce such curves whenever possible. Moreover, it is shown that the arc length of the interpolants depends on only one of the parameters, and that four (general) helical PH quintic interpolants always exist, corresponding to extrema of the arc length. Motivated by the desire to improve the fairness of interpolants to general data at reasonable computational cost, three selection criteria are proposed. The first criterion is based on minimizing a bivariate function that measures how “close” the PH quintic interpolants are to a PH cubic. For the second criterion, one of the parameters is fixed by first selecting interpolants of extremal arc length, and the other parameter is then determined by minimizing the distance measure of the first method, considered as a univariate function. The third method employs a heuristic but efficient procedure to select one parameter, suggested by the circumstances in which the “ordinary” cubic interpolant is a PH curve, and the other parameter is then determined as in the second method. After presenting the theory underlying these three methods, a comparison of empirical results from their implementation is described, and recommendations for their use in practical design applications are made.     

基于知识粗糙熵的快速属性约简算法

针对基于正域的属性约简算法在约简过程中存在重复计算属性相对重要度从而导致算法效率低的问题,从属性度量和搜索策略的角度提出基于知识粗糙熵的快速属性约简算法。首先,在决策信息系统中通过引入知识距离提出知识粗糙熵以度量知识的粗糙程度;其次,利用知识粗糙熵作为属性显著度的评价标准来评估单个属性的重要程度;最后,利用属性重要度对所有条件属性进行排序,且通过属性依赖度删除冗余属性,从而实现快速约简。在六个公开数据集上将所提算法与其他三种算法在运行效率和分类精度上进行对比实验。结果表明,该算法的运行效率比其他三种算法分别提高了83.24%、28.77%和59.92%;在三种分类器中,分类精度分别平均提高了0.83%、0.63%和1.37%。因此,所提算法在保证分类性能的同时,能以更快的速度获得约简。     

A complete parameterization of all positive rational extensions ofa covariance sequence

In this paper we formalize the observation that filtering and interpolation induce complementary, or “dual,” decompositions of the space of positive real rational functions of degree less than or equal to n. From this basic result about the geometry of the space of positive real functions, we are able to deduce two complementary sets of conclusions about positive rational extensions of a given partial covariance sequence. On the one hand, by viewing a certain fast filtering algorithm as a nonlinear dynamical system defined on this space, we are able to develop estimates on the asymptotic behavior of the Schur parameters (1918) of positive rational extensions. On the other hand we are also able to provide a characterization of all positive rational extensions of a given partial covariance sequence. Indeed, motivated by its application to signal processing, speech processing, and stochastic realization theory, this characterization is in terms of a complete parameterization using familiar objects from systems theory and proves a conjecture made by Georgiou (1983, 1987). Our basic result, however, also enables us to analyze the robustness of this parameterization with respect to variations in the problem data. The methodology employed is a combination of complex analysis, geometry, linear systems, and nonlinear dynamics     

Constraint solving for interpolation

Interpolation is an important component of recent methods for program verification. It provides a natural and effective means for computing the separation between the sets of ‘good’ and ‘bad’ states. The existing algorithms for interpolant generation are proof-based: They require explicit construction of proofs, from which interpolants can be computed. Construction of such proofs is a difficult task. We propose an algorithm for the generation of interpolants for the combined theory of linear arithmetic and uninterpreted function symbols that does not require a priori constructed proofs to derive interpolants. It uses a reduction of the problem to constraint solving in linear arithmetic, which allows application of existing highly optimized Linear Programming solvers in a black-box fashion. We provide experimental evidence of the practical applicability of our algorithm.     

Pareto optimal sensor locations for structural identification

Theoretical and computational issues arising in the selection of the optimal sensor configuration for parameter estimation in structural dynamics are addressed. The objective is to optimally locate sensors in the structure such that the resulting measured data are most informative for estimating the parameters of a family of mathematical model classes used for structural modeling. For a single model class, the information entropy is used as the optimality criterion for selecting the best sensor configuration. For multiple model classes, the problem is formulated as a multi-objective optimization problem of finding the Pareto optimal sensor configurations that simultaneously minimize appropriately defined information entropy indices. A heuristic algorithm is proposed for constructing effective Pareto optimal sensor configurations that are superior, in terms of computational efficiency and accuracy, to the Pareto sensor configurations predicted by evolutionary algorithms suitable for solving general multi-objective optimisation problems. The theoretical developments and the effectiveness of the proposed algorithms are illustrated for a 10-DOF chain-like spring mass model and a 32-DOF truss structure.     

用最大熵原则作多阈值选择的条件迭代算法

最大熵原则是图像处理中阈值选择的最常用方法之一.很多研究者针对最大熵原则作了各种研究,但一般都回避了用最大熵原则进行多阈值选择时运算量太大的问题.该文提出了解决这一问题的条件迭代ICM(iterated conditional modes)算法.通过实验比较了ICM算法与H.D.Cheng等人提出的模拟退火算法,充分显示了ICM算法的有效性.