Some sets of orthogonal polynomial kernel functions

Kernel methods provide high performance in a variety of machine learning tasks. However, the success of kernel methods is heavily dependent on the selection of the right kernel function and proper setting of its parameters. Several sets of kernel functions based on orthogonal polynomials have been proposed recently. Besides their good performance in the error rate, these kernel functions have only one parameter chosen from a small set of integers, and it facilitates kernel selection greatly. Two sets of orthogonal polynomial kernel functions, namely the triangularly modified Chebyshev kernels and the triangularly modified Legendre kernels, are proposed in this study. Furthermore, we compare the construction methods of some orthogonal polynomial kernels and highlight the similarities and differences among them. Experiments on 32 data sets are performed for better illustration and comparison of these kernel functions in classification and regression scenarios. In general, there is difference among these orthogonal polynomial kernels in terms of accuracy, and most orthogonal polynomial kernels can match the commonly used kernels, such as the polynomial kernel, the Gaussian kernel and the wavelet kernel. Compared with these universal kernels, the orthogonal polynomial kernels each have a unique easily optimized parameter, and they store statistically significantly less support vectors in support vector classification. New presented kernels can obtain better generalization performance both for classification tasks and regression tasks.     

基于提升小波和自适应多项式拟合的传感器网络多模数据压缩算法

为提高无线传感器网络的感知精度,提出了一种基于提升小波变换和自适应多项式拟合的多模数据压缩算法（adaptive multiple-modalities data compression algorithm based on lifting wavelet and adaptive polynomial fitting,简称AMLP）。在给定相关度阈值的前提下,AMLP算法先对数据进行灰色关联聚类,再对类中的相关数据进行自适应的多项式拟合,然后把未拟合的特征数据抽象成一个矩阵,利用提升小波变换去除数据的时间和空间相关性。最后,通过游程编码对数据作进一步压缩。仿真结果表明,AMLP算法能够有效去除不同数据间的冗余信息以及同种数据间的时间和空间冗余信息,提高压缩比,降低网络能耗。与基于小波的自适应多模数据压缩算法（adaptive multiple-modalities data compression algorithm based on wavelet,简称AMMC）相比,AMLP算法的数据恢复精度大大优于AMMC算法,压缩比和能耗相近。因此,AMLP算法更适用于要求高精度数据的传感器网络应用,如地质灾害监测、医疗和军事领域。     

New coprimeness over multivariable polynomial matrices and its application to control delay systems

This paper presents a new notion of coprimeness over multivariable polynomial matrices, where a single variable is given priority over the remaining variables. From a characterization through a set of common zeros of the minors, it is clarified that the presented coprimeness is equivalent to weakly zero coprimeness in the particular variable. An application of the presented coprimeness to control systems with non-commensurate delays and finite spectrum assignment is also presented. Because the presented coprimeness is stronger than minor coprimeness, non-commensurate delays are difficult to deal with in algebraic control theory. The “rational ratio condition” between delays, which can reduce non-commensurate delays to commensurate delays, proves to be both powerful and practical concept in algebraic control theory for delay systems.     

基于DPT的非线性调频信号的DOA估计

提出了一种基于DPT的宽带非线性调频信号的DOA估计算法.首先将非线性调频(NLFM)信号建模为高阶多项式相位信号(PPS)模型,然后通过高阶瞬时矩进行多项式相位变换.接收信号将变换为单个正弦信号和新的噪声.再利用ROOT-MUSIC或者ESPRIT算法对变换得到的正弦信号的波达方向进行估计.理论分析和仿真结果表明,该...     

Computing monodromy via continuation methods on random Riemann surfaces

We consider a Riemann surface X defined by a polynomial f(x,y) of degree d, whose coefficients are chosen randomly. Hence, we can suppose that X is smooth, that the discriminant δ(x) of f has d(d−1) simple roots, Δ, and that δ(0)≠0, i.e. the corresponding fiber has d distinct points {y₁,…,y_d}. When we lift a loop 0∈γ⊂C−Δ by a continuation method, we get d paths in X connecting {y₁,…,y_d}, hence defining a permutation of that set. This is called monodromy.Here we present experimentations in Maple to get statistics on the distribution of transpositions corresponding to loops around each point of Δ. Multiplying families of “neighbor” transpositions, we construct permutations and the subgroups of the symmetric group they generate. This allows us to establish and study experimentally two conjectures on the distribution of these transpositions and on transitivity of the generated subgroups.Assuming that these two conjectures are true, we develop tools allowing fast probabilistic algorithms for absolute multivariate polynomial factorization, under the hypothesis that the factors behave like random polynomials whose coefficients follow uniform distributions.     

火星着陆任务落点误差快速分析方法

在火星着陆任务设计中,为高效地评估进入点导航误差等动力学方程中的不确定因素对着陆精度的影响,引入Galerkin投影法则,提出一种火星着陆落点不确定度快速预报与分析方法．该方法采用Wiener-Askey正交多项式逼近着陆器状态,将落点误差的预报问题转换为1个确定性微分方程的求解问题．最后以我国未来实施火星着陆探测为背景,分别用本文算法和蒙特卡洛方法进行数值仿真分析,结果表明,本文算法能够对落点偏差进行快速分析,准确预测其均值和方差,且计算效率明显提高．     

Computation of the effects of uncertainty in volatility on option pricing and hedging

We quantify the effect of uncertainty in the volatility parameter σ on the Black–Scholes price of the European and American put. We apply probabilistic uncertainty analysis to the Black–Scholes model and compare the results with those of the Uncertain Volatility model. From historical data, we calibrate a probability distribution for the volatility. We then use Monte Carlo (MC) and a surrogate Polynomial Chaos (PC)/MC method to compute uncertainty bounds. The calibrated probability distribution is not one related to a standard orthogonal basis, so a basis is constructed numerically for the PC approximation. We show how to construct one stably from the probability distribution. We show that both methods give the same results, and quantify the relative speedup of the surrogate method. Finally, we investigate the effect of the parametric uncertainty, and show, for example, that the presence of uncertainty smoothes out the optimal exercise boundary of the American put.     

Polynomial solutions of certain differential equations

In this paper, the Chebyshev matrix method is applied generalisations of the Hermite, Laguerre, Legendre and Chebyshev differential equations which have polynomial solution. The method is based on taking the truncated Chebyshev series expansions of the functions in equation, and then substituting their matrix forms into the result equation. Thereby the given equation reduces to a matrix equation, which corresponds to a system of linear algebraic equations with unknown Chebyshev coefficients.     

矩形网格上Barycentric-Newton型混合有理插值

将重心有理插值与Newton型多项式插值结合起来,利用偏差商的递推算法,得到了满足矩形网格上所给插值条件的二元有理插值函数,给出了插值的特征性质和对偶形式.该二元有理插值函数它继承了重心有理插值的计算量小、没有极点、数值稳定性好和多项式插值的线性性质等优点.最后通过数值例子验证了所给方法的有效性.     

基于新障碍参数更新的二阶Mehrotra型预估—校正算法

针对二阶Mehrotra型预估-校正算法的一种变型算法,本文介绍一种新的自适应障碍参数更新法。利用该更新方法提出了相应的算法。新算法与之前的二阶Mehrotra型预估-校正算法相比,不用根据预估步和校正步的步长来确定参数的更新,而是在每步迭代中都采用自适应更新。最后证明了该算法在没有引进任何＂保障措施＂的情况下也具有相同的多项式时间复杂度。