基于多子空间线性判别分析的特征提取与分类

为了解决高维小样本数据的分类中Fisherface思想判别分析方法的不足,在最大散度差准则的基础上,提出了利用多线性子空间技术对每类样本进行单独描述的方法,该方法能更准确地反映样本在类内类间的分布关系.在分类中不是以距离作为判别依据,而是按照贝叶斯决策规则得到的隶属置信度作为衡量标准.实验结果表明了该方法的有效性,和同类方法相比,有更高的识别率.     

Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has handled nonlinear identification problems, the so called support vector regression. In SVMs designs for nonlinear identification, a nonlinear model is represented by an expansion in terms of nonlinear mappings of the model input. The nonlinear mappings define a feature space, which may have infinite dimension. In this context, a relevant identification approach is the least squares support vector machines (LS-SVMs). Compared to the other identification method, LS-SVMs possess prominent advantages: its generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of the competing methods, and more importantly, the performance does not depend on the dimensionality of the input data. Consider a constrained optimization problem of quadratic programing with a regularized cost function, the training process of LS-SVM involves the selection of kernel parameters and the regularization parameter of the objective function. A good choice of these parameters is crucial for the performance of the estimator. In this paper, the LS-SVMs design proposed is the combination of LS-SVM and a new chaotic differential evolution optimization approach based on Ikeda map (CDEK). The CDEK is adopted in tuning of regularization parameter and the radial basis function bandwith. Simulations using LS-SVMs on NARX (Nonlinear AutoRegressive with eXogenous inputs) for the identification of a thermal process show the effectiveness and practicality of the proposed CDEK algorithm when compared with the classical DE approach.     

巧用差分阻抗公式求近端串扰

从串扰的原理及串扰和差分阻抗之间的内在联系出发,通过模型变换和公式推导,给出了近端串扰系数和差分阻抗之间的关系式。再以平衡结构的带状线为例,得到了平衡结构带状线的近端串扰的估算公式。在相同条件下与HyperLynx仿真软件结果进行比较,验证发现估算结果与仿真结果基本符合。     

一次同余方程组的一种矩阵解法

同余方程组是数论中一个极为有用的课题。文中考虑的是具有一个未知数而具有不同的模的一次同余方程组。一般可以用中国剩余定理或者递推算法等方法给出一次同余方程组的解法。利用矩阵的初等变换和矩阵的Sm ith标准形给出了求解一次同余方程组的一种矩阵解法。     

静态随机存储器单粒子翻转效应的二维数值模拟

采用MEDICI二维模拟软件对静态存储器SRAM的单粒子翻转(SEU)现象进行了计算。对MOSFET漏区模拟得到的结果与电荷漏斗模型相吻合,表明所建立的物理模型的正确性。通过输入不同粒子的线性能量传输LET值,得到了某一结构器件的收集电荷与LET值的关系曲线,并给出临界电荷7 73×10-14C。     

测控系统中软件抗干扰方法的应用

介绍了测控系统中，根据各点采样数据似合出线性函数并过滤采样数据的干扰以提高系统可靠性。     

多层理论在圆筒型直线感应电动机计算上的应用

本文从麦克斯韦方程出发，在圆柱坐标系下建立了圆筒型直线电机的多层理论分析模用该模型对一台样机进行实例计算，并与磁阻抗法和实验值进行了比较，计算结果表明该方法的正确性和有效性。     

导控溢流阀动态数学模型的建立及动态性能分析

分析了导控型溢流阀的动态特性，建立了其运动状态的数学模型，并对微分方程进行拉氏变换，得出了动态系统的方框图。     

联合回归估计的优良性

讨论了联合回归估计的性质，证明了它是最小二乘估计的压缩估计，且有比它更好的抗干扰性和稳健性。     

基于观测器的非线性系统神经网络鲁棒控制

提出一类不依赖于模型的状态观测器，通过分析其根轨迹和极点要求配置合适的参数，该观测器本身是一个能提取高阶微分的高阶微分器．基于Lyapunov稳定性理论设计了使闭环系统渐近稳定，对模型变化和扰动具有鲁棒性的神经网络自适应控制器．该控制器不仅考虑了闭环系统的输出和设定输入误差的微分，而且考虑了误差的高阶微分，从而提高了控制品质．最后通过仿真例子验证了所提出理论的正确性．