基于Unscented变换的非线性状态平滑

众所周知, 平滑因可以使用更多的量测信息而能够获得比滤波更精确的状态估计. 为此, 本文提出一种面向非线性随机系统的状态平滑新算法. 首先, 基于最小方差估计理论, 提出了一种新颖的最优平滑器, 该平滑器为解决非线性状态平滑问题提供了一种通用的理论框架;接着, 采用Unscented变换(UT)来近似上述最优平滑框架中的平滑增益, 进而设计出一种次优平滑算法;最后, 相比传统扩展卡尔曼平滑器(EKS), 仿真结果验证了新算法的有效性和可行性.     

一种改进亮度平滑滤波调节图像融合算法

针对基于亮度平滑滤波调节(SFIM)遥感图像融合存在的空间细节信息提高不足,光谱存在一定失真的问题,提出了一种改进SFIM算法.该算法在SFIM基础上,结合平滑滤波和加权平均等方法,对SFIM的不足进行了改进;但保持了SFIM算法简单,运算快捷的优点.实验结果表明改进算法在空间细节增加,对比度以及光谱保持方面有了明显改善.     

一类非线性控制系统的输出调节

对一类非线性控制系统的输出调节问题,首先通过推广的精确线性化方法,将其转化为一类特殊的受扰线性控制系统对非线性系统的跟踪问题,然后利用可解性的概念解决了该问题,得到了非线性系统输出调节问题的局部和全局的结果。     

基于神经网络的非线性系统的输出调节

非线性系统输出调节问题可解的充要条件是存在一个中心流形满足某个非线性偏微分方程。本文用多层前向神经网络求解该非线性偏微分方程，从而逼近非线性系统的中心流形。在此基础上，构造状态反馈控制律，实现非线性系统的输出调节。     

磁传感器的非线性误差修正技术

介绍了最小二乘法多项式曲线拟合的原理和方法,以及该方法在磁传感器误差修正中的应用。对磁传感器的测量数据进行处理,利用计算机的高级语言计算拟合函数的系数,设定拟合准确度,得到函数的系数,将系数转换成十六进制数,保存到仪器的微计算机中,利用汇编语言编制误差修正程序,对测量误差进行自动修正。     

非线性控制系统的全局输出调节

讨论了非线性控制系统的全局输出调节.首先推广精确线性化方法,通过状态反馈 和微分同胚将非线性系统的全局输出调节问题,转化为线性系统对非线性系统的跟踪问题. 通过提出可解性的概念,得到线性系统对非线性系统全局跟踪的条件,该结果是线性系统结 果的推广.在反馈同胚变换全局成立条件下,得到非线性控制系统全局输出调节问题的充分 条件,该条件对外部系统只做较弱的可解性假设,在反馈同胚变换局部成立的条件下,可得局 部结果.     

Functional coefficient autoregressive models for vector time series

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simulations. For illustration, we apply the methods to a series of annual temperatures and tree ring widths. Computational issues are also briefly discussed.     

Linux系统引导概述

Linux操作系统在当今有着广泛的应用,系统引导是操作系统启动不可或缺的重要步骤。文中描述了在X86体系结构的系统中,从系统加电的一刻起,Linux内核是如何逐步加载到内存中并开始初始化的,通过对Linux启动部分源代码和相关文档的分析,介绍了BIOS读取引导扇区代码和实模式下初始化例程加载内核映像的过程。这对于嵌入式操作系统引导程序开发具有一定参考作用。     

Tests of Long Memory: A Bootstrap Approach

Many time series in diverse fields have been found to exhibit long memory. This paper analyzes the behaviour of some of the most used tests of long memory: the R/S analysis, the modified R/S, the Geweke and Porter-Hudak (GPH) test and the detrended fluctuation analysis (DFA). Some of these tests exhibit size distortions in small samples. It is well known that the bootstrap procedure may correct this fact. Here I examine the size and power of those tests for finite samples and different distributions, such as the normal, uniform, and lognormal. In the short-memory processes such as AR, MA and ARCH and long memory ones such as ARFIMA, p-values are calculated using the post-blackening moving-block bootstrap. The Monte Carlo study suggests that the bootstrap critical values perform better. The results are applied to financial return time series.     

基于Bootstrap的反舰导弹靶场试验小样本数据分析方法研究

自助法（Bootstrap）和随机加权法（Bayes Bootstrap）都能较好地处理小样本数据,其无先验性,以及计算过程中只需要实际观测数据的优越性,使其广泛地应用于实际数据处理之中,后者的估计精度要更好些.但对连续情况而言,自助法的计算特性使得重抽样本局限在原始样本范围内,无法渐进于真实情况.文章基于自助法研究了用改进的样本经验分布函数来解决这个问题,并通过仿真算例说明方法的有效性.     

复杂电磁干扰环境下的C6000 DSP自举方法

对TMS320C620X／670XDSP的存储结构和特点进行分析,采用分两次生成ROM文件的方式实现TMS320C620X／670XDSP自举,并阐述了该自举方法的关键过程。通过该自举方法,程序在片内RAM中执行,预置参数和常量从外部ROM中读取。结合软件三模冗余,该自举方法可以防止复杂电磁干扰环境下单粒子翻转效应。该方法已得到实际应用,行之有效。     

The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors

This paper is concerned with statistical inference for the coefficient of the linear regression model when the error term follows an autoregressive (AR) model. Past studies have reported severe size distortions, when the data are trending and autocorrelation of the error term is high. In this paper, we consider a test based on the bias-corrected bootstrap, where bias-corrected parameter estimators for the AR and regression coefficients are used. For bias-correction, the jackknife and bootstrap methods are employed. Monte Carlo simulations are conducted to compare size and power properties of the bias-corrected bootstrap test. It is found that the bias-corrected bootstrap test shows substantially improved size properties and exhibits excellent power for most of cases considered. It also appears that bootstrap bias-correction leads to better size and higher power values than jackknife bias-correction. These results are found to be robust to the choice of parameter estimation methods.JEL classifications: C12, C15, C63     

直流无刷电机自举驱动控制研究

针对永磁直流无刷电机(BLDCM),利用自举原理实现电机驱动,通过电机位置传感器进行换相,常规的换相方法造成电机无法自启动。为了解决上述问题,结合直流无刷电机换相特点和驱动电路本身特性,重点研究自举驱动的原理,分析电机无法自启动的原因,并找出解决方法,给出在自举电路的驱动下,直流无刷电机自启动的驱动波形。结果表明,该方法实现了直流无刷电机的自启动,且对电机的正常运行不产生影响。     

网络引导程序的设计和实现

利用PC机BIOS网络启动功能,可以实现远程裸机安装Windows操作系统和远程无盘启动,实现这些功能的核心技术是网络引导程序的设计。论文详细陈述了网络引导程序的设计思路,对其中涉及的引导过程,虚拟磁盘和映像文件等关键技术实现,予以详细介绍。按上述技术所开发的远程裸机安装Windows操作系统软件取得了良好效果。     

Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap

This article outlines a Bayesian bootstrap method for case based imprecision estimates in Bayes classification. We argue that this approach is an important complement to methods such as k-fold cross validation that are based on overall error rates. It is shown how case based imprecision estimates may be used to improve Bayes classifiers under asymmetrical loss functions. In addition, other approaches to making use of case based imprecision estimates are discussed and illustrated on two real world data sets. Contrary to the common assumption, Bayesian bootstrap simulations indicate that the uncertainty associated with the output of a Bayes classifier is often far from normally distributed.     

Fast online algorithm for nonlinear support vector machines and other alike models

Paper presents a unique novel online learning algorithm for eight popular nonlinear (i.e., kernel), classifiers based on a classic stochastic gradient descent in primal domain. In particular, the online learning algorithm is derived for following classifiers: L1 and L2 support vector machines with both a quadratic regularizer w ^t w and the l ₁ regularizer |w|₁; regularized huberized hinge loss; regularized kernel logistic regression; regularized exponential loss with l ₁ regularizer |w|₁ and Least squares support vector machines. The online learning algorithm is aimed primarily for designing classifiers for large datasets. The novel learning model is accurate, fast and extremely simple (i.e., comprised of few coding lines only). Comparisons of performances of the proposed algorithm with the state of the art support vector machine algorithm on few real datasets are shown.