Finite-time consensus of time-varying nonlinear multi-agent systems

This paper investigates the problem of leader–follower finite-time consensus for a class of time-varying nonlinear multi-agent systems. The dynamics of each agent is assumed to be represented by a strict feedback nonlinear system, where nonlinearities satisfy Lipschitz growth conditions with time-varying gains. The main design procedure is outlined as follows. First, it is shown that the leader–follower consensus problem is equivalent to a conventional control problem of multi-variable high-dimension systems. Second, by introducing a state transformation, the control problem is converted into the construction problem of two dynamic equations. Third, based on the Lyapunov stability theorem, the global finite-time stability of the closed-loop control system is proved, and the finite-time consensus of the concerned multi-agent systems is thus guaranteed. An example is given to verify the effectiveness of the proposed consensus protocol algorithm.     

A kernel-based centroid classifier using hypothesis margin

The centroid-based classifier is both effective and efficient for document classification. However, it suffers from over-fitting and linear inseparability problems caused by its fundamental assumptions. To address these problems, we propose a kernel-based hypothesis margin centroid classifier (KHCC). First, KHCC optimises the class centroids via minimising hypothesis margin under structural risk minimisation principle; second, KHCC uses the kernel method to relieve the problem of linear inseparability in the original feature space. Given the radial basis function, we further discuss a guideline for tuning the value of its parameter. The experimental results on four well-known data-sets indicate that our KHCC algorithm outperforms the state-of-the-art algorithms, especially for the unbalanced data-set.     

通用和修正D-H法在运动学建模中的应用分析

通用D-H法在机器人运动学标准建模中虽然已被广泛研究和应用,但其存在一个根本缺陷就是,对于两个相邻的平行关节,通用D-H法得到的齐次变换矩阵将是一个奇异矩阵,不能得到正确的模型,这样便无法对这部分关节运动进行正确的运动分析.而修正的D-H法是正对通用D-H所存在的缺陷进行了改进,保证了模型准确性.本文研究了通用的运动学建模方法D-H法和该方法的应用缺陷,以及修正的D-H法对通用D-H法存在的缺陷进行的改进,并对修正的D-H法在自由度串联机器人正逆运动学中建立模型的正确性进行了验证.     

基于R型聚类-因子分析的指标体系简化方法

评价指标体系过于复杂,容易产生冗余信息,增加计算与分析的难度.本文针对评价指标体系的简化方法展开研究,提出了基于R型聚类-因子分析的代表元提取方法.该方法具有定量控制代表元的信息丢失率、代表元实际含义易于解释等优点,同时避免了因子分析难以处理独立指标的问题.实验结果表明本文所提出的R型聚类–因子分析指标体系简化方法可以在没有先验知识可用的情况下,有效提高指标体系的简明性与有效性,同时保持指标信息的完备性.     

基于联合特征匹配的多视角三维重建方法

本研究提出了一种基于角点与直线联合特征的三阶段匹配算法,依次经过相关匹配、松弛迭代匹配和最小平方中值法匹配三个步骤,并与此同时加入限定图像匹配区域、添加手工匹配点对以及局部直线匹配三个人机交互环节,将局部坐标系中得到的数据点进行三维数据融合到一个坐标系中,能较好解决视差不连续区域和遮挡区域的误匹配问题.实验证明该算法具有良好的运行效率和稳定性,能够对非平面物体进行精确三维重建并实现多视角显示.     

基于综合法的火炮方向机齿轮传动误差分析

方向机对火炮射击精度影响很大,齿轮传动误差是方向机传动误差的主要来源。恰当的分析方法能提高方向机齿轮传动设计精度与效率。本文将齿轮传动误差分为静态误差与动态误差。结合理论分析,用概率分析法计算出静态误差;用有限元分析法计算出动态误差,最后得到总的传动误差。基于分析结果采样拟合出不同载荷下的误差曲线。将这种分析方法应用到火炮方向机齿轮传动误差分析中,得到了该火炮方向机齿轮传动在变载荷下的误差曲线。该分析方法为火炮方向机齿轮传动设计提供参考。为火炮其他部件的设计提供一种思路。     

A globally and quadratically convergent primal–dual augmented Lagrangian algorithm for equality constrained optimization

We present a primal–dual augmented Lagrangian method to solve an equality constrained minimization problem. This is a Newton-like method applied to a perturbation of the optimality system that follows from a reformulation of the initial problem by introducing an augmented Lagrangian function. An important aspect of this approach is that, by a choice of suitable updating rules of parameters, the algorithm reduces to a regularized Newton method applied to a sequence of optimality systems. The global convergence is proved under mild assumptions. An asymptotic analysis is also presented and quadratic convergence is proved under standard regularity assumptions. Some numerical results show that the method is very efficient and robust.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

Self-consistent gradient flow for shape optimization

We present a model for image segmentation and describe a gradient-descent method for level-set based shape optimization. It is commonly known that gradient-descent methods converge slowly due to zig–zag movement. This can also be observed for our problem, especially when sharp edges are present in the image. We interpret this in our specific context to gain a better understanding of the involved difficulties. One way to overcome slow convergence is the use of second-order methods. For our situation, they require derivatives of the potentially noisy image data and are thus undesirable. Hence, we propose a new method that can be interpreted as a self-consistent gradient flow and does not need any derivatives of the image data. It works very well in practice and leads to a far more efficient optimization algorithm. A related idea can also be used to describe the mean-curvature flow of a mean-convex surface. For this, we formulate a mean-curvature Eikonal equation, which allows a numerical propagation of the mean-curvature flow of a surface without explicit time stepping.