基于混合算法的七轴机器人逆解求解

为了解决七轴机器人逆运动学求解较难、精度较低的问题,提出一种基于数值迭代法与粒子群优化算法相结合的方法求解.对机器手模型及逆运动学位姿函数进行了研究,利用数值迭代法进行机器手逆解的初步最优角度解,在此基础上缩小搜索范围利用粒子群算法进行二次优化搜索计算,减小数值迭代过程中产生的累积误差,仿真结果表明机器手的位姿精度大大...     

On the pole of non-square transfer function matrix Moore–Penrose pseudo-inverses

An essential step in many controller design approaches is computing the inverse of the plant. For a square plant, its inverse is stable if the plant is minimum phase (MP). Nevertheless, this conclusion does not hold for a non-square plant. In this paper, the pole feature of the Moore–Penrose pseudo-inverse of a non-square transfer function matrix is analysed. Instead of complicated advanced mathematical tools, only basic results of polynomial theory and the Binet–Cauchy theorem are used in the analysing procedure. The condition for testing the stability of the Moore–Penrose pseudo-inverse of an MP non-square transfer function matrix is given. This condition implies that the Moore–Penrose pseudo-inverse of a non-square transfer function matrix cannot be directly used as the optimal controller. Numerical examples are provided to illustrate the correctness of the condition.     

A duality relationship between fuzzy metrics and metrics

Based on the duality relationship between indistinguishability operators and (pseudo-)metrics, we address the problem of establishing whether there is a relationship between the last ones and fuzzy (pseudo-)metrics. We give a positive answer to the posed question. Concretely, we yield a method for generating fuzzy (pseudo-)metrics from (pseudo)-metrics and vice versa. The aforementioned methods involve the use of the pseudo-inverse of the additive generator of a continuous Archimedean t-norm. As a consequence, we get a method to generate non-strong fuzzy (pseudo-)metrics from (pseudo-)metrics. Examples that illustrate the exposed methods are also given. Finally, we show that the classical duality relationship between indistinguishability operators and (pseudo)-metrics can be retrieved as a particular case of our results when continuous Archimedean t-norms are under consideration.     

经典网和自由网平差的基准及其在变形分析中的应用

在经典网和自由网平差理论的基础上,推导和综述了经典网、伪逆网和拟稳网平差的基准问题,提出了在变形分析应用过程中基准的选择及变形观测网形发生变化时的数据处理方法。     

Hermite前向神经网络隐节点数目自动确定

从函数逼近论出发,构造了一类以Hermite正交基为激励函数的前向神经网络.在保证网络逼近能力的前提下,令其输入层至隐层的权值和各神经元阈值分别为1和0,导出了基于伪逆的隐层至输出层最优权值的直接计算公式.并针对Hermite前向神经网络,提出一种依照学习精度要求而逐次递增型的隐节点数自动、快速、准确的确定算法.对多个目标函数的计算机仿真和预测结果表明,该神经网络权值直接确定方法和隐节点数自动确定算法能很快地找到最优的隐节点数及其对应的最优权值,且网络具有较好的预测能力.     

Method of Hybrid Approximations for Modelling of Multidimensional Nonlinear Systems

In this paper we propose a new approach to the constructive mathematical representation of nonlinear systems transforming stochastic signals. The approach is based on a combination of a new best approximation technique and a new iterative procedure. For each iteration, the approximation is constructed as a polynomial operator of degree r which minimizes the mean–squared error between a desired output signal and the output signal of the approximating system. We show that this hybrid technique produces a computationally efficient and flexible method for modelling of nonlinear systems. The method has two degrees of freedom, the degree r of the approximating operator and the number of iterations, to decrease the associated error.     

处理水准自由网平差基准变化的新方法

对于水准自由网平差，提出了在特定条件下进行第Ⅱ期平差的方法，可以任意改变基准，然而平差结果正确，位移量正确．先从理论上给出论证，后以算例验证．解决了两期网基准变化的问题．     

图像雅可比矩阵伪逆估计在视觉伺服中的应用

在一般无标定视觉伺服算法的基础上，提出了一种基于递推最小二乘法估计图像雅可比矩阵伪逆的无标定视觉伺服算法．这种方法无需计算雅可比矩阵伪逆．针对固定目标，文中给出了比例控制算法的稳定性条件．针对运动目标，推导了基于图像雅可比矩阵伪逆估计的伪高斯—牛顿视觉伺服算法和信任区视觉伺服算法．最后,仿真验证了三种视觉伺服算法的有效性．     

大型飞机神经网络重构控制

介绍了一种通过神经网络在线进行飞行控制重构的方案.本文以非线性动态逆方法为基本控制律,通过设计的自适应神经网络,实时补偿因操纵面故障而引起的逆误差.最后的仿真结果表明,该方案是可行的.     

RBF神经网络的多特征融合目标跟踪算法

本文提出一种基于RBF神经网络的多特征融合目标跟踪算法。RBF神经网络是一种简单且高效的三层神经网络,可以大大提高跟踪效率。首先利用引入空间相关性的三维颜色直方图、Canny算子以及灰度图多个特征来构造RBF神经网络的输入特征向量;然后,采用三角核函数作为RBF神经网络的激活函数;最后,利用所提算法对目标进行跟踪。实验结果表明,所提算法能够对目标进行可靠跟踪,对相机移动、光照变化、目标旋转、形状变形等问题有很好的适应性。