条件收敛的

Based on the analysis of the mechanism of robot manipulators, a trajectory tracking controlling research model is first built up with ADAMS, and its mathematical model is formulated through the study of robot kinematics and dynamics. After the modeling errors are analyzed in practical robotic systems, a conception of centralized error is brought forward according to the theory of weighting functions. In the case of modeling errors' exist, the robotic uncertain model is derived by introducing an auxiliary control variable into inverse dynamic analysis. The control strategy of robust exponential convergence is applied to the robotic uncertain model, the applicable conditions and the applicable controller with this application are presented. The stable control effect under three main model uncertainties (parametric errors, joint disturbs, joint frictions) are systematically studied on the previously built research model. The problems of robust exponential convergence controller which often results in unstable output and so produces a large relative error when the input trajectory is in a small range are resolved by adjusting the control parameters based on the controller's structure.

本文在机械手的机械结构分析基础上，利用ADAMS建立了用于机械手轨线跟踪控制研究的机械手模型；通过对机器人运动学和动力学问题的研究，建立了机械手研究的数学模型；分析了机器人系统中模型误差的主要来源，根据加权函数法分析，提出了模型集中误差的概念；在存有模型误差的情况下，采用逆动力学结构并引入辅助控制量，由机器人误差模型推导了机器人非确定性模型；提出了采用鲁棒指数收敛法对上述机器人非确定性模型进行鲁棒镇定的控制策略，并给出了对机器人进行鲁棒指数收敛控制时系统不确定因素应满足的匹配条件；针对机械手研究模型，深入研究了鲁棒指数收敛控制器对机器人系统中常见的模型参数误差、关节扰动、关节摩擦等不确定因素的镇定控制效果；针对鲁棒指数收敛控制器易产生控制量振荡的问题以及在小范围内系统轨线跟踪的稳态误差过大问题，本文分别提出了基于控制器结构的控制参数调整法和基于轨线跟踪范围大小的控制参数分段切换法。

However, Hacking convincingly argues that the theorem of convergence of opinions is not about the convergence of a posterior probability Pre, but about the convergence of a conditional probability Pr.

然而，哈金有说服力地表明，意见收敛定理证明的是条件概率Pr的收敛，而不是验后概率Pre的收敛。

Furthermore, the first-order optimality condition and its equivalent reformulations for generalized semi-infinite max-min programming with a non-compact set are presented using the lower-Hadamard directional derivative and subdifferential.2. Chapter 3 studies the gradient-type methods for unconstrained optimization problems. Section 1 proposes a new class of three-term memory gradient methods. The global convergence property of the method is established. Furthermore, in order to improve the convergence property of the method, a new class of memory gradient projection methods is presented with the property that the whole sequence of iterates converges to a solution to the problem under the conditions such as pseudo-convexity and continuous differentiability of objective function. In section 2, two new classes of methods, called gradient-type method with perturbations and hybrid projection method with perturbations, are proposed. In these methods, non-monotone line search technique is employed, which makes them easily executed in computer.

第3章研究了无约束优化问题的梯度型算法，第1节提出了一类新的三项记忆梯度算法，讨论了算法的全局收敛性，进一步提出了一类新的具有更好收敛性质的记忆梯度投影算法，并证明了该算法在函数伪凸的情况下具有整体收敛性，第2节在非单调步长搜索下提出了带扰动项的梯度型算法及其混合投影算法，这两类算法的一个重要特征就是步长采用线搜索确定而不象许多文献中那样要求步长趋于零，这样更容易在计算机上实现，在较弱的条件下证明了这些算法的全局收敛性，数值算例表明了算法的有效性。

In chapter 1, we discuss several iterative methods and their convergence conditions. While, we also present the techniques in proving the convergence theorem.

第一章，主要对几种迭代法的收敛性进行了讨论，总结了它们的收敛条件及证明各种迭代法收敛性的技巧。

The variational convergence of real function sequence is extended to vector function sequence and the lower semicontinuity of approximating set of weak Pareto solutions of a given multiobjective decision making problem with general constraint set is obtained by using the variational convergence.

本文将函数序列的v－收敛性推广到向量值函数，在v－收敛性的条件下得到了给定的多目标决策问题的近似弱有效解集的下半连续性并给出了若干容易验证的充分条件。

In this paper, the imprecise proofs existing in some literatures are firstly pointed out. Then, the local convergence is proved in a new way and the condition of convergence to the local maximum point is offered. Finally, the geometrical counterexamples are provided for explanation about convergence of Mean Shift and the conclusion is further discussed.

首先指出了Comaniciu和李乡儒的证明过程存在错误；然后，从数学上重新证明了Mean Shift算法的局部收敛性，并指出其收敛到局部极大值的条件；最后，从几何上举反例分析了Mean Shift的收敛性，并进行了深入比较和讨论。

Global and local superlinear/quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs.

基于此给出了求解箱约束变分不等式的一种阻尼牛顿算法，在较弱的条件下，证明了算法的全局收敛性和局部超线性收敛率，以及对线性箱约束变分不等式的有限步收敛性。

A damped Newton type method was presented based on it.Global and local superlinear/quadratic convergence results were obtained under mild conditions, and the finite termination property was also shown for the linear BVIs.

基于此给出了求解箱约束变分不等式的一种阻尼牛顿算法，在较弱的条件下，证明了算法的全局收敛性和局部超线性收敛率，以及对线性箱约束变分不等式的有限步收敛性。

With the idea of smoothing Newton method, we propose a new class of smoothing Newton methods for the nonlinear complementarity problem based on a class of special functions. In this paper, complementarity problem is converted into a series of smoothing nonlinear equations and a modified smoothing Newton algorithm is used to solve the equations. We use Newton direction and Gradient direction together in the algorithm which guarantees that our method is globally convergent. Also using another smoothing function, we reformulate the generalized nonlinear complementarity problems defined on a polyhedral cone as a system of smoothing equations and a smooth unconstrained optimization problem. Theoretical results that relate the stationary points of the merit function to the solution of the generalized nonlinear complementarity problems are presented, we use the modified smoothing Newton algorithm in generalized nonlinear complementarity problems, under mild hypothesis, a global convergence is proved.

本文一方面基于现有的各种光滑Newton法的思想和半光滑理论，利用著名的F-B互补函数的光滑形式，首先将互补问题的求解转化为求解一系列光滑的非线性方程组，然后给出了一种修正的光滑Newton法，该方法不仅放宽对函数F的要求，在Newton方程不可解时引入初始效益函数的最速下降方向，而且光滑因子的选择也比较简单可行，同时在适当的条件下，证明了其算法具有全局收敛性；另一方面，借助另一种F-B光滑函数，将多面体锥上的广义互补问题转化为一种光滑形式，讨论了优化问题的稳定点与广义非线性互补问题的解之间的理论关系，并将这种修正的光滑Newton法用于求解广义非线性互补问题中，在适当的条件下，该算法同样具有全局收敛性。

The empirical Bayes estimation for parameters of the one-side truncated distribution family with convergence rate which can close to 1 arbitrarily is investigated using NA samples and an example that satisfies the conditions of theorem is given.

运用NA样本密度函数核估计构造了一类截断型分布族参数的经验Bayes估计，建立了它的收敛速度，证明了在适当条件下该收敛速度可以任意接近于1，文中还给出了适合定理条件的例子。

Plunder melds and run with this jewel!

掠夺melds和运行与此宝石！

My dream is to be a crazy growing tree and extend at the edge between the city and the forest.

此刻，也许正是在通往天国的路上，我体验着这白色的晕旋。