高阶隐马氏模型研究进展

首先分析了高阶隐马氏模型的研究动机和背景,并给出了高阶隐马氏模型的一般结构和定义,然后总结了高阶隐马氏模型的一般研究方法,最后展望了高阶隐马氏模型进一步的研究方向.     

Lagrange非结构网格高阶交错型守恒气体动力学格式

提出Lagrange(拉格朗日)非结构网格高阶交错型守恒气体动力学格式.用产生于当前时刻子网格密度和网格声速的子网格压力和MUSCL方法构造了高阶子网格力,利用高阶子网格力构造了高阶空间通量,借助时间中点通量的Taylor(泰勒)展开完成了高阶时间通量离散.研制了Lagrange非结构网格高阶交错型守恒气体动力学格式.对Saltzman活塞问题等进行了数值模拟,数值结果显示了Lagrange非结构网格高阶交错型守恒气体动力学格式的有效性和精确性.     

齐次Morrey-Herz空间上交换子的有界性

本文研究了高阶交换子的有界性, 利用截断算子方法和函数分解技术, 在齐次Morrey-Herz空间上, 得到了由次线性算子与BMO函数生成的高阶交换子的有界性以及卷积类算子高阶交换子的有界性.     

Gram-Charlier展开在动态金融高阶矩建模中的近似误差

Gram-Charlier展开(GCE)在动态条件高阶矩GARCH模型中得到了广泛应用。相比于常见的分布,GCE的高阶矩形式更加直观,能更直接地刻画条件高阶矩的动态特征。此展开函数并不非负,在实际应用时需要平方并归一化,但已有文献大多忽略此处理后高阶矩所发生的变化。本文研究了平方处理方法对展开函数高阶矩的影响,推导了正确的高阶矩形式。实证研究表明,用原始参数作为近似高阶矩会产生显著偏误,在VaR预测时会严重低估风险。     

高阶Bernoulli多项式和高阶Euler多项式的关系

利用发生函数的方法，讨论了高阶Bernoulli数和高阶Euler数，高阶Bernoulli多项式和高阶Euler多项式之间的关系，得到了经典Bernoulli数和Euler数，经典Bernoulli多项式和Euler多项式之间的新型关系。     

一类离散非线性系统基于幂次函数的显式高阶滑模预测控制算法

针对一类不确定离散非线性系统,提出了一种显式高阶滑模预测控制算法:利用幂次函数趋近律和高阶滑模控制方法,结合预测控制策略,给出了一种高阶滑模预测模型,得到了显式的高阶滑模预测模型控制器,使其兼具滑模控制方法与预测控制方法的优点,有效削弱了滑模控制系统的抖振现象.最后用数值仿真算例证明了文章设计方法的有效性,与传统滑模预测控制相比,收敛速度更快,稳定性更好.     

带跳随机波动率模型的高阶ADI分裂格式

针对带跳随机波动率模型满足的偏积分微分方程,提出一种新的高阶交替方向隐式（ADI）有限差分格式,该模型是一个具有混合导数和非常数系数的对流扩散型初边值问题.我们将不同的高阶空间离散与时间步ADI分裂格式相结合,得到了一种空间四阶精度、时间二阶精度的有效方法,并采用Fourier方法分析了高阶ADI格式的稳定性.最后,通过对欧式看跌期权定价模型进行数值实验证实了数值方法的高阶收敛性.     

高阶微分中值定理初探

本文对高阶中值定理进行了初步的探讨,提供了标准的高阶中值定理的解决方法,也对如何处理一些非标准情形提供了一些思路.从这些探讨来看,高阶中值定理尚有许多值得进一步探索之处.     

基于数学建模问题的高阶抽象思维能力培养探析

数学高阶抽象思维能力的培养是中学数学的重要目标之一,对学生的数学学习和发展具有重要的作用和价值.本文旨在探讨高中数学建模问题的数学高阶抽象思维能力的培养方法,介绍了高阶抽象思维能力在高中数学学科中的重要性和在教学中所面临的挑战,提出了具体的培养方法和措施.     

高阶Camassa—Holm方程的一类行波解

研究高阶Camassa-Holm方程的行波解,采用一种新的方法求解行波方程,获得了高阶Camassa-Holm方程的一类行波解．     

Implicit Extension of Taylor Series Method with Numerical Derivatives

The Taylor series method is one of the earliest analytic‐numeric algorithms for approximate solution of initial value problems for ordinary differential equations. The main idea of the rehabilitation of these algorithms is based on the approximate calculation of higher order derivatives using well‐known technique for the partial differential equations. The implicit extension based on a collocation term added to the explicit truncated Taylor series. This idea is different from the general collocation method construction, which led to the implicit R‐K algorithms [1].     

Taylor series method for dynamical systems with control: Convergence and error estimates

To optimize a complicated function constructed from a solution of a system of ordinary differential equations (ODEs), it is very important to be able to approximate a solution of a system of ODEs very precisely. The precision delivered by the standard Runge-Kutta methods often is insufficient, resulting in a “noisy function” to optimize. We consider an initial-value problem for a system of ordinary differential equations having polynomial right-hand sides with respect to all dependent variables. First we show how to reduce a wide class of ODEs to such polynomial systems. Using the estimates for the Taylor series method, we construct a new “aggregative” Taylor series method and derive guaranteed a priori step-size and error estimates for Runge-Kutta methods of order r. Then we compare the 8,13-Prince-Dormand’s, Taylor series, and aggregative Taylor series methods using seven benchmark systems of equations, including van der Pol’s equations, the “brusselator,” equations of Jacobi’s elliptic functions, and linear and nonlinear stiff systems of equations. The numerical experiments show that the Taylor series method achieves the best precision, while the aggregative Taylor series method achieves the best computational time. The final section of this paper is devoted to a comparative study of the above numerical integration methods for systems of ODEs describing the optimal flight of a spacecraft from the Earth to the Moon. __________ Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 24, Dynamical Systems and Optimization, 2005.     

Differential transformation approach to thermal conductive problems with discontinuous boundary condition

A two-dimensional differential transformation method is employed to reduce the partial differential equations of the non-continuous thermal conductive boundary value problem to a Taylor series in a polynomial form. The partial differential equations are solved by the two-dimensional T-spectra method of differential transformation and by the use of trial initial polynomial conditions. The investigative parameters include the time-step of the differential transformation and the number of sub-domain segments. The numerical simulation results indicate that the proposed approach using the two-dimensional T-spectra method of differential transformation is applicable to the solution of non-continuous thermal conductive boundary value problems.     

Numerical solution of the one-dimensional time-independent Schrödinger’s equation by recursive evaluation of derivatives

We develop a simple numerical method for solving the one-dimensional time-independent Schrödinger’s equation. Our method computes the desired solutions as Taylor series expansions of arbitrarily large orders. Instead of using approximations such as difference quotients for the derivatives needed in the Taylor series expansions, we use recursive formulas obtained using the governing differential equation itself to calculate exact derivatives. Since our approach does not use difference formulas or symbolic manipulation, it requires much less computational effort when compared to the techniques previously reported in the literature. We illustrate the effectiveness of our method by obtaining numerical solutions of the one-dimensional harmonic oscillator, the hydrogen atom, and the one-dimensional double-well anharmonic oscillator.     

A Chebyshev interval method for nonlinear dynamic systems under uncertainty

This paper proposes a new interval analysis method for the dynamic response of nonlinear systems with uncertain-but-bounded parameters using Chebyshev polynomial series. Interval model can be used to describe nonlinear dynamic systems under uncertainty with low-order Taylor series expansions. However, the Taylor series-based interval method can only suit problems with small uncertain levels. To account for larger uncertain levels, this study introduces Chebyshev series expansions into interval model to develop a new uncertain method for dynamic nonlinear systems. In contrast to the Taylor series, the Chebyshev series can offer a higher numerical accuracy in the approximation of solutions. The Chebyshev inclusion function is developed to control the overestimation in interval computations, based on the truncated Chevbyshev series expansion. The Mehler integral is used to calculate the coefficients of Chebyshev polynomials. With the proposed Chebyshev approximation, the set of ordinary differential equations (ODEs) with interval parameters can be transformed to a new set of ODEs with deterministic parameters, to which many numerical solvers for ODEs can be directly applied. Two numerical examples are applied to demonstrate the effectiveness of the proposed method, in particular its ability to effectively control the overestimation as a non-intrusive method.     

Effective matrix formalism for singularity analysis of differential equations and new intergrable system in nonlinear elasticity

We introduce a simple matrix formalism for Taylor series and generalized Laurent series that can be used for implementing the Taylor method for nonlinear ODEs and singularity analysis of differential equations. Advantages of this approach over conventional techniques are shown on model examples. Surprisingly, the same formalism can be used for proving C-integrability of a 3D model in nonlinear elasticity. An alternative proof is obtained by using similarity between the model in nonlinear elasticity and the classic Pohlmeier-Lund-Regge model from high energy physics. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Insufficiency of chemical network model integration using a high-order Taylor series method

The rate of change for the concentrations of chemical substances in a set of reactions is modeled by a nonlinear dynamical system, which warrants the use of numerical integration methods for differential equations. Previous work advocates the use of a specialized high-order Taylor series method because of an observed reduction in computation time. Contrastingly, we show combinatorial and computational difficulties of the standard Taylor series method, which may dramatically increase computational time or reduce the quality of output. We provide two implementations, a naïve algorithm and an algorithm employing dynamic programming; we are able to overcome only some numerical obstacles and therefore conclude that the Taylor series approach is insufficient for large sets of reactions having many chemical substances.     

Solution of Two-Point Boundary-Value Problems Using the Differential Transformation Method

This paper considers two-point boundary-value problems using the differential transformation method. An iterative procedure is proposed for both the linear and nonlinear cases. Using the proposed approach, an analytic solution of the two-point boundary-value problem, represented by an mth-order Taylor series expansion, can be obtained throughout the prescribed range.     

Breaking the limits: The Taylor series method

This paper discusses several examples of ordinary differential equation (ODE) applications that are difficult to solve numerically using conventional techniques, but which can be solved successfully using the Taylor series method. These results are hard to obtain using other methods such as Runge-Kutta or similar schemes; indeed, in some cases these other schemes are not able to solve such systems at all. In particular, we explore the use of the high-precision arithmetic in the Taylor series method for numerically integrating ODEs. We show how to compute the partial derivatives, how to propagate sets of initial conditions, and, finally, how to achieve the Brouwer’s Law limit in the propagation of errors in long-time simulations. The TIDES software that we use for this work is freely available from a website.     

Numerical solution of the Burgers’ equation by automatic differentiation

We compute the solution of the one-dimensional Burgers’ equation by marching the solution in time using a Taylor series expansion. Our approach does not require symbolic manipulation and does not involve the solution of a system of linear or non-linear algebraic equations. Instead, we use recursive formulas obtained from the differential equation to calculate exact values of the derivatives needed in the Taylor series. We illustrate the effectiveness of our method by solving four test problems with known exact solutions. The numerical solutions we obtain are in excellent agreement with the exact solutions, while being superior to other previously reported numerical solutions.