Tu族演化方程的换位表示

本文利用特征值的泛函梯度方法，先给出Ｔｕ谱问题的Ｌｅｎａｒｄ算子对；尔后通过求解一个关键性的算子方程，得到Ｔｕ谱问题的演化方程族之换位表示。     

获得非线性演化方程Backlund变换的一种新的途径

本文给出一种求非线性演化方程Backlund变换的方法,应用于非线性演化方程时,得到了与WTC方法一致的Backlund变换,避开了WTC方法涉及到的递推关系和截尾的讨论.     

一类非线性发展方程整体弱解的存在性和稳定性

该文考虑一类新的非线性方程(｜ut｜r-2ut)t-Δutt-Δu-ρ(t)Δut=f(u) 的初边值问题，利用小扰动法证明了整体弱解的 存在性，借用位势井的概念得到了解的稳定性.〖HT5”H〗关键词:〖HT5”SS〗非线性发展方程;初边值问题;整体弱解;稳定性.     

Lamé函数和非线性演化方程的新多级准确解

基于Lamé方程和新的Lamé函数,应用摄动方法和Jacobi椭圆函数展开法求解非线性演化方程,获得多种新的多级准确解.这些解在极限条件下可以退化为各种形武的孤波解.     

知识创造行为与组织惯例的演化博弈及其仿真研究

以演化博弈模型为主要理论工具,在对知识创造行为与组织惯例关系予以描述的基础上,构建知识创造行为与组织惯例的演化博弈模型。通过求解复制动态方程,分析不同条件下知识创造行为与组织惯例分别达到演化稳定均衡的策略。研究结果表明:知识创造行为与组织惯例的匹配属于动态、重复博弈过程,参与博弈的预期收益、激励成本、转换成本直接决定演化稳定策略且影响个体对知识创造行为与组织惯例的选择,知识创造行为则倾向以承袭为主的保守策略。演化博弈方法的引入为知识创造行为和组织惯例的研究开辟了全新视角,也为相关领域的进一步探索提供有利的理论支持。     

四元数矩阵的L[AKo¨D]wner偏序

对于A，B∈〖WTHX〗H〖WTBX〗(n,≥)，该文给出L[AKo¨D]wner偏序下A≤〖DD(〗L〖DD)〗B的五种刻画和A\+2≤〖DD(〗L〖DD)〗B\+2的两种刻画；并将A，B∈〖WTHX〗C〖WTBX〗(n,*)时，A≤〖DD(〗L〖DD)〗B的Liski定理推广到四元数除环上．     

有限振幅T-S波在非平行边界层中的非线性演化研究

研究对非平行边界层稳定性有重要影响的非线性演化问题,导出与其相应的抛物化稳定性方程组,发展了求解有限振幅T-S波的非线性演化的高效数值方法。这一数值方法包括预估-校正迭代求解各模态非线性方程并避免模态间的耦合,采用高阶紧致差分格式,满足正规化条件,确定不同模态非线性项表和数值稳定地作空间推进。通过给出T-S波不同的初始幅值,研究其非线性演化。算例与全Navier-Stokes方程的直接数值模拟(DNS)的结果作了比较。     

IT外包知识共享行为的演化博弈分析

针对IT外包知识共享障碍的问题,在已有IT外包知识共享研究的基础上,依据演化博弈论建立了IT外包知识共享行为的演化博弈模型,通过求解复制动态方程与演化稳定策略,分析了影响IT外包知识共享行为的因素,并利用Matlab 7.6软件进行仿真。研究结果表明,知识可共享量、知识互补程度、信任、知识吸收转化能力、知识共享成本和风险以及激励机制等是影响IT外包知识共享行为的关键因素。最后提出相应的对策与建议。     

广义Ginzburg-Landau方程和Rangwala Rao方程的显式精确解

该文通过适当代换并结合假设待定法，求出了具高阶非线性项的Liénard方程a″(ξ)+la(ξ)+ma\+\{2p+1\}(ξ)+na\+\{4p+1\}(ξ)=0的三类精确解. 据此求出了广义Ginzburg Landau方程、Rangwala Rao方程及若干 导数schr〖AKo¨D〗dinger型方程的孤波解和三角函数型周期波解.     

非线性演化方程显式精确解的新算法

本给出了一种求解非线性演化方程的新算法。将这种算法运用于变形浅水波方程,获得了八组显式精确解,其中包括新的孤波解和周期解。借助于Ｍａｔｈｅｍａｔｉｃａ软件,这种算法能够在Ｃｏｍｐｕｔｅｒ上实现。     

Numerical Solution of the Fokker-Planck Equation with Time-Discontinuous Galerkin Methods

The response of non-linear dynamic systems under white noise excitation possesses Markov characteristics. The evolution of the probability density of the system response is represented by the Fokker-Planck equation, which characterizes advection and diffusion. The solution probability density distribution often possesses high gradients. Therefore an efficient numerical solution technique based on a discontinuous Galerkin approximation in the time domain is proposed for the solution of the Fokker-Planck equation. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Predicting stochastic characteristics of generalized eigenvalues via a novel sensitivity-based probability density evolution method

This paper proposes a novel numerical method for predicting the probability density function of generalized eigenvalues in the mechanical vibration system with consideration of uncertainties in structural parameters. The eigenproblem of structural vibration is presented by first and the sensitivity of generalized eigenvalues with respect to structural parameters can be derived. The probability density evolution method is then developed to capture the probability density function of generalized eigenvalues considering uncertain material properties. Within the proposed method, the probability density evolution equation for the generalized eigenvalue problem is established accounting for the sensitivity of generalized eigenvalues with respect to structural parameters. A new variable which connects generalized eigenvalues to structural parameters is then introduced to simplify the original probability density evolution equation. Next, the simplified probability density evolution equation is solved by using the finite difference method with total variation diminishing schemes. Finally, the probability density function as well as the second-order statistical quantities of generalized eigenvalues can be predicted. Numerical examples demonstrate that the proposed method yields results consistent with Monte-Carlo simulation method within significantly less computation time and the coefficients of variation of uncertain parameters as well as the total number of them have remarkable effects on stochastic characteristics of generalized eigenvalues.     

Modelling Fruit Characteristics During Apple Maturation: A Stochastic Approach

At present, mathematical models to predict the change of fruit quality attributes during apple maturation are deterministic and do not take into account the large natural variability of fruit quality attributes during the growing season. In this work a stochastic system approach was developed to describe the quality evolution of fruit. The basic dynamics of fruit quality evolution was represented by means of a stochastic system, in which the initial conditions and the model parameters were specified as random variables together with their probability density functions. A fundamental approach from stochastic systems theory was used to compute the propagation of the probability density functions of fruit quality attributes, which requires the numerical solution of the Fokker–Planck equation.     

Investigations on the numerical solution of the Fokker-Planck equation with discontinuous Galerkin Methods

Nonlinear dynamic systems under stochastic excitation possess Markov characteristics. Thus, their stochastic equation of motion can be transformed into the Fokker-Planck equation which describes the evolution of the probability density. A discontinuous Galerkin (DG) method is applied to solve the Fokker-Planck equation. This method provides numerical stability as well as accuracy and is able to treat discontinuities of the solution. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Transient behaviour of semilinear stochastic systems with random parameters

The transient solution of a class of nonautonomous, stochastic differential equations with given random initial conditions is studied. The considered evolution equation is characterized by the presence of a deterministic linear term and of a random nonlinear term whose parameters can be modeled by random processes of Rice noise type. Analytical approximated expressions for the first- and second-order moments and for the probability density of the solution process are derived and are applied to determine the statistical properties of a class of stochastic nonlinear oscillators.     

The probability density function to the random linear transport equation

We present a formula to calculate the probability density function of the solution of the random linear transport equation in terms of the density functions of the velocity and the initial condition. We also present an expression for the joint probability density function of the solution in two different points. Our results have shown good agreement with Monte Carlo simulations.     

On the generalization of probabilistic transformation method

The probabilistic transformation method with the finite element analysis is a new technique to solve random differential equation. The advantage of this technique is finding the “exact” expression of the probability density function of the solution when the probability density function of the input is known. However the disadvantage is due to the characteristics (geometrics and materials) of the analyzed structure included in the random differential equation.

In this paper, a developed formula is used to generalize this technique by obtaining the “exact” joint probability density function of the solution in any situations, as well as the proposed technique for the non-linear case.     

A probabilistic theory of random maps

We present a probabilistic theory of random maps with discrete time and continuous state. The forward and backward Kolmogorov equations as well as the FPK equation governing the evolution of the probability density function of the system are derived. The moment equations of arbitrary order are derived, and the reliability and first passage time problem are also studied. Examples are presented to demonstrate the application of the theoretical development. Numerical solutions including the time histories of moment evolution, steady state probability density function, reliability and first passage time probability density function for time discrete random maps are included. The present work compliments the existing theory of continuous time stochastic processes.     

The Riesz–Bessel Fractional Diffusion Equation

This paper examines the properties of a fractional diffusion equation defined by the composition of the inverses of the Riesz potential and the Bessel potential. The first part determines the conditions under which the Green function of this equation is the transition probability density function of a Lévy motion. This Lévy motion is obtained by the subordination of Brownian motion, and the Lévy representation of the subordinator is determined. The second part studies the semigroup formed by the Green function of the fractional diffusion equation. Applications of these results to certain evolution equations is considered. Some results on the numerical solution of the fractional diffusion equation are also provided.     

Smoothing algorithms for nonlinear finite-dimensional systems

Systems are considered where the state evolves either as a diffusion process or as a finitestate Markov process, and the measurement process consists either of a nonlinear function of the state with additive white noise or as a counting process with intensity dependent on the state. Fixed interval smooting is considered, and the first main result obtained expresses a smoothing probability or a probability density symmetrically in terms of forward filtered, reverse-time filterd and unfiltered quantities; an associated result replaces the unfiltered and reverse-time filtered qauantities by a likelihood function. Then stochastic differential equationsare obtained for the evolution of the reverse-time filtered probability or probability density and the reverse-time likelihood function. Lastly, a partial differential equation is obtained linking smoothed and forward filterd probabilities or probability densities; in all instances considered, this equation is not driven by any measurement process. The different approaches are also linked to known techniques applicable in the linear-Gaussian case.