几类高阶线性微分方程亚纯解的增长性

本文研究了几类亚纯函数系数的高阶线性微分方程解的增长性问题,得到了齐次和非齐次线性微分方程亚纯解增长性的精确估计.     

一类高阶微分方程亚纯解的增长性

研究了几种类型的高阶线性亚纯系数微分方程的亚纯解的增长性,对方程的亚纯解的增长率得到了精确估计.     

双模Jordan KdV方程的多孤子解与精确解

根据简化的Hirota双线性方法和cole-hopf变换,当双模Jordan KdV方程中的非线性参数与线性参数取特殊值时,得到了双模Jordan KdV方程的多孤子解.同时,当方程中非线性参数与线性参数取一般值,也得到了这个方程的其它的精确解.     

一类新的线性二次随机最优控制器的设计

给出一类正倒向随机微分方程解的存在唯一性结果,应用这个结果研究了一类新的推广的随机线性二次最优控制器的设计问题,得到了由正倒向随机微分方程解所表示的唯一最优控制器的显式结构;在推广的Riccati方程系统基础上,得到最优控制器精确的线性反馈形式.最后,给出了随机线性二次最优控制器的设计算法.     

复域高阶线性微分方程解的某些振荡结果

本文研究了线性微分方程解的增长性.运用值分布理论,得到方程解的增长的精确估计.进一步,讨论了上述方程解以及解的一阶,二阶导数与小函数的关系.     

一类海-气振子ENSO模型的同伦分析解法

研究了一个ENSO海-气时滞振子模型.利用同伦分析方法,得到了该模型解的近似展开式,通过与特殊情况下的精确解比较,得到的二级近似解具有较高的精度.     

一类随机线性互补问题的求法

考虑一类随机互线性补问题的求解方法,目的是通过定义NCP函数来使正则化期望残差最小化.通过拟蒙洛包洛方法产生一系列观察值并且证得离散近似问题最小值解的聚点就是相应随机线性互补问题的期望残差最小值ERM,同时得到利用ERM到解为有界的充分条件.进一步证明ERM法能够得到具有稳定性和最小灵敏度的稳健解.     

一类高阶微分方程亚纯解的下级、超级、二级不同零点收敛指数

本文研究了一类高阶亚纯系数非齐次及齐次线性微分方程的复振荡.在亚纯解的极点重数无限制的前提下,得到了方程亚纯解的下级、超级、二级不同零点收敛指数等的精确估计.改进了陈宗煊、Ki-HoKwon等的结果.     

某类线性微分方程亚纯解的增长性

研究了一类亚纯函数系数的线性微分方程的解的增长性问题,得到了齐次和非齐线性微分方程亚纯解的增长级、超级、二级不同零点收敛指数的精确估计．     

一维3×3非线性系统脉冲波的干扰

利用非线性几何光学的方法,讨论一维情形下3×3半线性偏微分方程具有两个初始脉冲波的干扰问题.证明方程组近似解的存在性,得到脉冲波的干扰出现在一阶项中.通过对近似解进行误差分析,得到精确解的一个好的近似.     

Automatic Differentiation to Facilitate Maximum Likelihood Estimation in Nonlinear Random Effects Models

Maximum likelihood estimation in random effects models for non-Gaussian data is a computationally challenging task that currently receives much attention. This article shows that the estimation process can be facilitated by the use of automatic differentiation, which is a technique for exact numerical differentiation of functions represented as computer programs. Automatic differentiation is applied to an approximation of the likelihood function, obtained by using either Laplace's method of integration or importance sampling. The approach is applied to generalized linear mixed models. The computational speed is high compared to the Monte Carlo EM algorithm and the Monte Carlo Newton–Raphson method.     

A partially linearized sigma point filter for latent state estimation in nonlinear time series models

A new technique for the latent state estimation of a wide class of nonlinear time series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment-matching algorithm and then a linear programming based procedure is used in the update step of the state estimation. The effectiveness of the new filtering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process.     

A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution

Remaining useful life (RUL) estimation is regarded as one of the most central components in prognostics and health management (PHM). Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults. In this paper we consider the problem of estimating the RUL from observed degradation data for a general system. A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian updating and expectation maximization (EM) algorithm. The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this paper, which makes the estimated RUL depend on the observed degradation data history. As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of our presented approach. A major contribution under these two special cases is that our approach can obtain an exact and closed-form RUL distribution respectively, and the moment of the obtained RUL distribution from our presented approach exists. This contrasts sharply with the approximated results obtained in the literature for the same cases. To our knowledge, the RUL estimation approach presented in this paper for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history. Finally, numerical examples for RUL estimation and a practical case study for condition-based replacement decision making with comparison to a previously reported approach are provided to substantiate the superiority of the proposed model.     

On the general problem of mean estimation

Mean estimation for a random function is considered in its most general context, including both the case of infinite dimensional mean and exact estimation. The existence theorem for the best linear unbiased estimates is established. The general definition of the pseudobest estimate is given; necessary and sufficient conditions for the efficiency of the pseudobest estimates are obtained. As an application the problem of the periodic trend estimation is considered.     

An implicit least squares algorithm for nonlinear rational model parameter estimation

In this study a new insight into least squares regression is identified and immediately applied to estimating the parameters of nonlinear rational models. From the beginning the ordinary explicit expression for linear in the parameters model is expanded into an implicit expression. Then a generic algorithm in terms of least squares error is developed for the model parameter estimation. It has been proved that a nonlinear rational model can be expressed as an implicit linear in the parameters model, therefore, the developed algorithm can be comfortably revised for estimating the parameters of the rational models. The major advancement of the generic algorithm is its conciseness and efficiency in dealing with the parameter estimation problems associated with nonlinear in the parameters models. Further, the algorithm can be used to deal with those regression terms which are subject to noise. The algorithm is reduced to an ordinary least square algorithm in the case of linear or linear in the parameters models. Three simulated examples plus a realistic case study are used to test and illustrate the performance of the algorithm.     

关于L~1空间中Fredholm积分方程数值解的误差估计

将Fredholm积分方程的算法推广到更一般的情形,且证明其收敛性,并给出这种更精确算法的误差估计.数值算例进一步验证了算法的合理性.     

生理药动学模型参数估计问题

根据生理药动学模型的特点,把非线性药动学模型转化为线性模型,并验证线性模型的精确性.在此基础上,构造估计药动学模型参数的目标函数,并利用非线性优化算法求解模型参数.仿真结果表明,我们的算法具有快速、精确和稳定的特点.给出了一种快速估计复杂生理药动学模型参数的方法,这为解决复杂生理药动学模型的参数估计问题提供了一种有效工具.     

Weyl eigenvalue asymptotics and sharp adaptation on vector bundles

This paper examines the estimation of an indirect signal embedded in white noise on vector bundles. It is found that the sharp asymptotic minimax bound is determined by the degree to which the indirect signal is embedded in the linear operator. Thus when the linear operator has polynomial decay, recovery of the signal is polynomial where the exact minimax constant and rate are determined. Adaptive sharp estimation is carried out using a blockwise shrinkage estimator. Application to the spherical deconvolution problem for the polynomially bounded case is made.     

One-modulus residue arithmetic algorithm to solve linear equations exactly

The paper presents an error-free algorithm to solve linear equations using the residue arithmetic. Simultaneously with solving linear equation system, the exact value of determinant of the system matrix is also calculated. The algorithm removes roundoff errors and according to this kind of errors ensures stability of the solution. It is suitable for implementation for computers with possibility of vector operations.     

The problem of estimation in continuous/discrete dynamic systems

We consider the problem of optimal nonlinear estimation in a continuous/discrete dynamic system whose state vector is a piecewise-continuous function and the observations are represented by a collection of continuous and discrete processes. We obtain equations that determine the piecewise-continuous conditional probability density function of the process being estimated, on the basis of which we form optimal estimates, as well as an exact representation of the solution of these equations and corresponding estimation algorithms for the problem of linear optimal continuous/discrete estimation. Translated fromDinamicheskie Sistemy, Vol. 11, 1992.