可信性空间上基于复模糊变量的学习理论的关键定理

给出了可信性测度空间上复模糊变量定义及一些性质;给出了复模糊变量的经验风险泛函、期望风险泛函以及经验风险最小化原则严格一致收敛的定义。在此基础上给出并证明了基于复模糊变量的学习理论的关键定理,为系统建立可信性空间上的复统计学习理论奠定了理论基础。     

一种改进的异步电动机直接转矩控制方法

针对传统异步电动机直接转矩控制方法转矩及磁链脉动大、开关频率不固定的缺点,提出了一种改进的异步电动机空间矢量调制直接转矩控制方法。该方法通过转矩角闭环控制实现定子磁链幅值和相角的解耦,从而获得参考电压空间矢量,实现对磁链误差和转矩误差的控制。仿真结果表明,该方法有效地克服了传统直接转矩控制方法的缺陷,获得了良好的动态响应性能。     

离散零空间算法在机织物仿真中的应用

针对机织物卡尔丹角多体模型维数较高,计算效率不理想的问题,提出采用离散零空间算法降低多体动力学方程维数,减少计算复杂度和约束违约所导致的数值漂移.首先结合离散零空间等效变换消去拉格朗日乘子项,然后进行结点参数化,实现两次系统降维,提高机织物仿真速度.通过KES剪切试验与仿真,验证了上述算法应用的可行性.通过仿真计算复杂度及效率特征指标对比,反映出优化方程的降维优势和计算高效,尤其适用于大型织物仿真.     

Biologically adaptive robust mean shift algorithm with Cauchy predator-prey BBO and space variant resolution for unmanned helicopter formation

Visual tracking technology can provide measurement information for unmanned helicopter formation and thus, more attention is being paid to this research area. We propose a novel mean shift （MS） algorithm that is both adaptive and robust for unmanned helicopter formation and apply it to the leading unmanned helicopter tracking. The movement of an unmanned helicopter is very flexible and changeable, which makes the tracking there of more difficulty than for common targets. In creating an algorithm that can adapt to the acceleration of the unmanned helicopter and estimates both the scale and orientation of the movement changes, we combine the traditional MS with the bio-inspired Cauchy predator-prey biogeography-based optimization （CPPBBO） evolutionary algorithm, and also the space variant resolution （SVR） mechanism of the human visual system （MS-CPPBBO-SVR）. To demonstrate the effectiveness and robustness of the proposed method and justify the importance of the CPPBBO algorithm and SVR mechanism at the same time, a series of comparative exper- iments were carried out. The experimental results of the proposed MS-CPPBBO-SVR method are compared with other competitive tracking methods, such as MS, MS with SVR （MS-SVR）, MS-SVR with several other optimization algorithms, and the robust particle filter algorithm. The experimental results demonstrate that our proposed tracking approach, MS-CPPBBO-SVR, is more adaptive, robust and efficient in target tracking than the other methods.     

Guaranteed transient performance based control with input saturation for near space vehicles

This paper describes the design of guaranteed transient performance based attitude control for the near space vehicle(NSV)with control input saturation using the backstepping method.To improve the robust controllability of the NSV,the parameter adaptive method is used to tackle the integrated effect of unknown time-varying disturbance and control input saturation.Based on the backstepping technique and parameter estimated outputs,a robust attitude control scheme is proposed for the NSV with input saturation.A novel robust attitude control scheme is then proposed based on a prescribed performance bound(PPB)which characterizes the convergence rate and maximum overshoot of the attitude tracking error.The closed-loop system stability under both the developed robust attitude control schemes is proved using Lyapunov’s method and uniformly asymptotical convergence of all closed-loop signals is guaranteed.Finally,simulation results are given to show the effectiveness of both the proposed robust constrained attitude control schemes.     

三种近似算子之间的关系

在广义近似空间中,可以从对象、知识粒以及子系统的角度构造3种不同类型的广义粗糙近似算子。文中研究了这些近似算子的基本性质与相互关系,给出了3类近似算子相同的充要条件。另外,不同的近似空间可能生成相同的基于知识粒及基于子系统的近似算子,文中给出了不同二元关系生成相同近似算子的一些充要条件。     

Probabilistic observation model correction using non-Gaussian belief fusion

This paper presents a framework for state estimation which tolerates uncertainty in observation model parameters by (1) incorporating this uncertainty in state observation, and (2) correcting model parameters to improve future state observations. The first objective is met by an uncertainty propagation approach, while the second is achieved by gradient-descent optimization. The novel framework allows state estimates to be represented by non-Gaussian probability distribution functions. By correcting observation model parameters, estimation performance is enhanced since the accuracy of observations is increased. Monte Carlo simulation experiments validate the efficacy of the proposed approach in comparison with conventional estimation techniques, showing that as model parameters converge to ground-truth over time, state estimation correspondingly improves when compared to a static model estimate. Because observation models cannot be known with perfect accuracy and existing approaches do not address parametric uncertainties in non-Gaussian estimation, this work has both novelty and usefulness in most state estimation contexts.     

基于LDA和word2vec的英文作文跑题检测

针对目前国内的英语作文辅助批阅系统缺少准确而高效的跑题检测算法的问题,提出了一种结合LDA和Word2vec的跑题检测算法。该算法利用LDA模型对文档建模并通过Word2vec对文档训练,利用得到的文档主题和词语之间的语义关系,对文档中各主题及其特征词计算概率加权和,最终通过设定合理阈值筛选出跑题作文。实验中通过改变文档的主题数而得到的不同F值,确定了最佳主题数。实验结果表明所提出的方法比基于向量空间模型的方法更具有效性,可以检测到更多的跑题作文并且准确率较高,F值达到89%以上,实现了作文跑题检测的智能化处理,可以有效地应用在英语作文教学中。     

SFT中的因素作用路径与作用历史

为了描述SFT中系统或元件在不同工作环境状态下经历变化的特征,提出了因素作用路径和作用历史的概念。作用路径描述系统或元件在不同工作状态变化过程中所经历状态的集合。作用历史描述经历作用路径过程中的可积累状态量。给出使用作用路径和作用历史分析问题的步骤。列举一例子,计算在设定条件下的元件维护成本。计算作用路径分别为AB和ACB两种,作用历史中使元件故障概率等于20%的维护成本,该维护成本是通过调整使用时间t和使用温度c产生的。最后给出以实例为背景的作用历史计算方法的不足、面临的问题和可能的解决方法。     

机器人相机空间智能编程方法研究*

针对传统的示教编程方式存在操作复杂,效率低,危险性高等不足,严重限制了工业机器人的推广应用。基于自然的人机交互示教方式,提出了一种基于计算机视觉的相机空间工业机器人智能虚拟编程方法,本方法不需要实际操作示教盒和机器人本体,仅采用辅助示教工具在视觉相机空间示教就实现了工业机器人的虚拟编程。主要研究了实现该方案的关键技术即基于相机空间映射模型的视觉定位技术以及基于K-means聚类算法实现的相机空间映射关系自学习技术。最后,基于自主开发的机器人平台,开展基于相机空间的虚拟智能编程实验,验证了本文提出的相机空间工业机器人智能编程方法的可行性及正确性。