刚体转动渐进跟踪控制的逆系统方法

逆系统方法是以非线性控制为目的,以直接分析为途径,以动态系统的逆概念为核心。该文将逆系统方法应用于刚体动力学中,运用非线性控制的逆系统方法设计Euler动力学方程的控制器,通过对一个陀螺的外力矩进行实施控制,使刚体在动坐标系下的角速度稳定跟踪指令,来实现对刚体转动的渐进跟踪控制。     

Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models

We propose a novel methodology for evaluating the accuracy of numerical solutions to dynamic economic models. It consists in constructing a lower bound on the size of approximation errors. A small lower bound on errors is a necessary condition for accuracy: If a lower error bound is unacceptably large, then the actual approximation errors are even larger, and hence, the approximation is inaccurate. Our lower‐bound error analysis is complementary to the conventional upper‐error (worst‐case) bound analysis, which provides a sufficient condition for accuracy. As an illustration of our methodology, we assess approximation in the first‐ and second‐order perturbation solutions for two stylized models: a neoclassical growth model and a new Keynesian model. The errors are small for the former model but unacceptably large for the latter model under some empirically relevant parameterizations.     

Inference in hybrid Bayesian networks using dynamic discretization

We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.     

欧拉函数φ（n）的两个估计式

探讨了当ｘ≥１，ｋ为负整数时，欧拉函数的ｋ次方和的估计式，并得到欧拉函数对数和的估计式，式中为素数，求和号下的ｐ过全体素数，，式中为素数，求和号下的ｐ过全体素数。     

带有马尔可夫调制和跳的随机微分延迟方程Euler方法的强相合性

本文主要研究了下列形式的随机微分延迟方程：dX（t） =f（X（t） ,X（t -τ（t） ） ,r（t） ）dt ＋g（X（t） ,X（t -τ（t） ） ,r（t） ）dW（t） ＋h（X（t） ,X（t -τ（t） ）, r（t） ）dN（t） 0≤t≤T.考虑了时间延迟．r（t）为变量，Euler方法数值解；给出并且证明了Euler方法的强相合性定理，即Euler方法数值解均方意义下局部收敛于精确解．     

Heine-euler extensions of the poisson distribution

The paper shows that the Heine and Euler distributions (Benkherouf and Bather, 1988) are members of a family of q-series anologues of the Poisson distribution, with similar probability mass functions, but different restrictions on their parameters, and different modes of genesis and properties. The relationships between the Heine, Euler, pseudo-Euler, Poisson and geometric distributions are explored. Illustrative data sets are discussed.     

普通逻辑教学中的周延性问题──欧拉图解判定法

本文针对学生对普通逻辑教学中词项的周延性问题的疑惑,利用欧拉图的判定方法,通过直观的形式,并从是否具有必然性的角度分析了性质判断中主谓项的周延性情况。本文认为此种方法具有很强的说服力,可为广大的师生所采纳。     

GMM with Weak Identification

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak‐identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.     

Concurrent generation of multivariate mixed data with variables of dissimilar types

ABSTRACT

Data sets originating from wide range of research studies are composed of multiple variables that are correlated and of dissimilar types, primarily of count, binary/ordinal and continuous attributes. The present paper builds on the previous works on multivariate data generation and develops a framework for generating multivariate mixed data with a pre-specified correlation matrix. The generated data consist of components that are marginally count, binary, ordinal and continuous, where the count and continuous variables follow the generalized Poisson and normal distributions, respectively. The use of the generalized Poisson distribution provides a flexible mechanism which allows under- and over-dispersed count variables generally encountered in practice. A step-by-step algorithm is provided and its performance is evaluated using simulated and real-data scenarios.     

基于DSP与LabVIEW仿生运动机理模型在线切换、离散化系统

仿生运动机理模型在实际的应用过程中,必须针对特定的应用场合和不同的硬件系统选择不同的控制模型和最优参数,使实际效果达到最佳。文章提出了一种基于DSP与LabVIEW,对仿生运动机理模型进行实时在线离散化的方法。该方法实现了控制模型的在线切换、参数的实时修正、模型的在线离散化、LabVIEW和DSP的实时通信。实验结果表明,该系统实时性好、运行可靠。