Robust Output Tracking Control of an Uncertain Linear System via a Modified Optimal Linear-Quadratic Method

This study investigates the robust output tracking problem for a class of uncertain linear systems. The uncertainties are assumed to be time invariant and to satisfy the matching conditions. According to the selected nominal parameters, an optimal solution with a prescribed degree of stability is determined. Then, an auxiliary input via the use of an adapting factor, connected to the nominal optimal control, is introduced to guarantee the robustness and prescribed degree of stability for the output tracking control of the uncertain linear systems. This method is very simple and effective and can reject bounded uncertainties imposed on the states. A maglev vehicle model example is given to show its effectiveness.     

大学物理实验的双任务教学方法初探

为搭建课程改革与学习效果之间的桥梁，在大学物理实验中引入了双任务教学方法．本文介绍了双任务教学法的概念、优点及具体实施方法．     

A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.     

优化灰导数白化值的无偏灰色GM(1,1)模型

通过优化灰导数白化值 ,建立了无偏的 GM(1,1)模型 ,给出了估计模型参数的方法 ,证明了无偏GM(1,1)模型具有白指数律重合性 ,提出了新的预测公式 .实例分析表明 ,新方法提高了模型的精度 ,扩大了模型的适用范围 .     

带有随机因素的逆DEA模型

本文讨论含有随机因素的逆 DEA模型 .逆 DEA模型解决的问题是 :对于某个决策单元 (DMU ) ,若增加其输入 ,在保持相对效率水平不变的情况下 ,估计 (预测 )输出应增加多少 .因此逆 DEA模型可用于短期预测问题 .带有随机因素的逆 DEA模型 ,是将该问题转化成机会约束的多目标规划问题 ,在某些特殊情况下 ,成为机会约束的线性规划问题 .     

将SVRT模型应用到单原子多原子反应F+CH2D2→CH2D/CHD2+DF/HF中

由于含时波包方法具有经典的直观又不乏量子力学的准确 ,选择含时波包方法来处理F +CH2 D2 →CH2 D/CHD2 +DF/HF反应 .把半刚性振转子 (SVRT)模型应用到该反应体系中 ,研究了两个通道中该反应从基态反应物开始在修正过的J1(MJ1)势能面上计算出来了反应几率、积分截面、速率常数 .反应几率随能量变化的图的数值结果给出了振荡结构 ,这些振荡结构是可以和动力学振荡联系起来的 .而这些振荡结构在积分截面随着能量变化的图中就被反应几率求和后的平均结果所掩盖了 .速率常数和实验结果的比较也得到了较好的结果 .     

Richards模型在蔬菜生长预测中的应用

本文从 Richards模型的数学表达出发 ,阐述了模型中各参数初始值的确定以及优化过程 .并利用菜花和菠菜的试验数据建立其“最优”生长模型 .     

A New Modal Lindström Theorem

We prove new Lindstr?m theorems for the basic modal propositional language, and for some related fragments of first-order logic. We find difficulties with such results for modal languages without a finite-depth property, high-lighting the difference between abstract model theory for fragments and for extensions of first-order logic. In addition we discuss new connections with interpolation properties, and the modal invariance theorem. Mathematics Subject Classification (2000): Primary 03B45; Secondary 03C95     

AGS能量下π~－π~－的Bose—Einstein关联

全同粒子间的玻色－爱因斯坦关联可以用来研究相对论性核碰撞中次级粒子源分布及有关的物理问题．本文用强子级联模型（ＨＣＭ）模拟２８Ｓｉ（１４．６ＡＧＣＶ／ｃ）＋Ａｕ反应，得到π－粒子的ｆｒｅｅｚｅ－ｏｕｔ状态，进而计算了π－π－关联函数，取得与实验一致的结果．还计算了ｆｒｅｅｚｅ—ｏｕｔπ－源的均方根线度，并讨论了它与拟合方法抽取的源参数间的关系．     

保险公司离散型崩溃模型研究

本文提出并讨论了含投资因素、红利分配因素的崩溃模型，得出了关于崩溃概率、保险公司的期望寿命的结论，这些结论推广了没有考虑投资因素的崩溃模型的相关结论．