用于组合测姿的Kalman渐消因子自适应估计算法

提出了适用于姿态测量的Kalman滤波渐消因子自适应估计算法,滤波中采用序贯处理的方法计算出每个量测量对应的渐消因子,在位置速度组合导航系统中,只有位置、速度的误差状态是直接可观测的,用序贯滤波处理计算得到的渐消因子对协方差阵中对应于位置和速度误差状态的对角元素进行自适应控制,抑制滤波发散,提高位置、速度和姿态的测量精度。半实物仿真表明,与原来的算法相比,修改后的方法不仅能够提供高精度位置、速度信息,而且还可以提供高精度姿态信息,其中航向误差在0.08°以下,俯仰和横滚误差在0.02°左右。     

降水预测的模糊权马尔可夫模型及应用

中长期降水量的预测是气象科学的一个难点问题，论文首先基于降水过程存在大量不确定性、不精确性的特点，应用模糊有序聚类的方法建立降水丰桔状况的分级标准；然后针对降水量为相依随机变量的特点，采取以规范化的各阶自相关系数为权重，用加权的马尔可夫链来预测未来降水的丰桔变化状况；在此基础上，根据模糊集理论中的级别特征值，计算出具体的降水量；最后以山西省河曲水文站近50年的降水资料为实例，对该方法进行了具体的应用，收到了较为满意的结果，从而为提高中长期降水量预报的精度提供了一条值得探索的途径。     

Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network

The Analytic Network Process (ANP) is a multicriteria theory of measurement used to derive relative priority scales of absolute numbers from individual judgments (or from actual measurements normalized to a relative form) that also belong to a fundamental scale of absolute numbers. These judgments represent the relative influence, of one of two elements over the other in a pairwise comparison process on a third element in the system, with respect to an underlying control criterion. Through its supermatrix, whose entries are themselves matrices of column priorities, the ANP synthesizes the outcome of dependence and feedback within and between clusters of elements. The Analytic Hierarchy Process (AHP) with its independence assumptions on upper levels from lower levels and the independence of the elements in a level is a special case of the ANP. The ANP is an essential tool for articulating our understanding of a decision problem. One had to overcome the limitation of linear hierarchic structures and their mathematical consequences. This part on the ANP summarizes and illustrates the basic concepts of the ANP and shows how informed intuitive judgments can lead to real life answers that are matched by actual measurements in the real world (for example, relative dollar values) as illustrated in market share examples that rely on judgments and not on numerical data. Thomas L. Saaty holds the Chair of University Professor, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, and obtained his Ph.D. in mathematics from Yale University. Before that he was a professor at the Wharton School of the University of Pennsylvania for ten years. Prior to that he spent seven years at the Arms Control and Disarmament Agency in the State Department in Washington, DC, that carried out the arms reduction negotiations with the Soviets in Geneva. His current research interests include decision-making, planning, conflict resolution and synthesis in the brain. As a result of his search for an effective means to deal with weapons tradeoffs at the Disarmament Agency and, more generally, with decision-making and resource allocation, Professor Saaty developed The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP). He is co-developer of the software Expert Choice and of the software Super Decisions for decisions with dependence and feedback. He has authored or co-authored twelve books on the AHP/ANP. Professor Saaty has also written a number of other books that embrace a variety of topics, including Modern Nonlinear Equations, Nonlinear Mathematics, Graph Theory, The Four Color Problem, Behavioral Mathematics, Queuing Theory, Optimization in Integers, Embracing the Future and The Brain: Unraveling the Mystery of How It Works. His most recent book is Creative Thinking, Problem Solving & Decision Making. The book is a rich collection of ideas, incorporating research by a growing body of researchers and practitioners, profiles of creative people, projects and products, theory, philosophy, physics and metaphysics...all explained with a liberal dose of humor. He has published more than 300 refereed articles in a wide variety of professional journals. He has been on the editorial boards of Mathematical Reviews, Operations Research, Naval Research Logistics Quarterly, Mathematical and Computer Modeling, Socio-Economic Planning Sciences, Applied Mathematics Letters, and several others. He also served as consultant to many corporations and governments.     

基于虚拟样机的异构双腿机器人联合仿真

阐述了一类新型双足机器人--异构双腿机器人的概念及研究目的.研究用Pro/E、ADAMS和MATLAB/Simulink联合构建异构双腿机器人的虚拟样机,并提出基于虚拟样机的联合仿真策略.为实现对期望步态的动态跟踪,提出了基于对角回归神经网络的自适应PID控制算法.为验证方案的合理性,进行了步态仿真及控制仿真.结果表明,虚拟样机技术可避免复杂的人工建模和求解过程,仿真逼真且接近实际系统,为复杂双足机器人系统提供了良好的研究方法.     

基于Hopfield网络单元机组负荷多变量模型算法控制

电厂负荷系统是一个有耦合和约束的多变量对象。模型算法控制(MAC)是预测控制的一种，在惯性迟延对象的控制中有很好的应用。该文提出用Hopfield网络来求解具有约束的多变量负荷系统的控制输入。仿真表明，该方法算法简单、鲁棒性强、控制效果理想。     

基于信息融合的汽车防撞决策系统研究

研究并提出了基于多传感器信息融合的动态目标跟踪算法来实现防撞系统的信息获取，并引入预测残留误差来修正跟踪残留误差不能完全反映不可预测的信息所带来的误差；研究了驾驶过程状态信息的表达方法，建立了汽车一环境状态特征模型，描述了汽车在行驶过程中时变动态的驾驶状态。在此基础之上，利用模糊积分方法融合多种相关信息，确定汽车应采用的安全运行模式，实现主动安全防撞决策。     

Epidemic Prediction of Hemorrhagic Fever with Renal Syndrome Using Grey Multivariable Model

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基于MPI的并行分布式高斯消元算法设计和评估

为了满足电磁仿真数值计算日益增高的速度和精度的需求,针对单机内存需求和计算负荷需求都比较大的矛盾,提出基于分布式并行机群环境的并行计算划分和并行存储划分的算法设计思想,并且给出了基于行列循环数据划分的并行计算算法描述.在此基础上进行了实验验证,用MPI+FORTRAN和MPI+C编程实现了对大矩阵求逆的分布式高斯消元,并进行了性能评估和实验验证,在国内外超级计算中心平台上的实验结果表明所完成的工作对于系统的电磁仿真计算具有应用价值,该算法和代码实现可应用于电磁仿真计算的矩量法MOM(Method of Mom)中.     

真空助力器带主缸总成疲劳试验系统辨识

真空助力器带主缸总成是汽车制动系统的关键增力部件,其气动疲劳试验系统由气动子系统、液压子系统、电控子系统等多个子系统构成,属于复杂的非线性系统。该系统存在众多不确定因素,因此无法直接建立数学模型。为了考察该系统的动态性能,采用递归BP神经网络对该系统进行辨识。试验结果表明,递归BP神经网络能够很好的逼近该复杂非线性系统,在不同时间序列作用下均有较好的泛化能力。     

基于火焰图像特征与BT-SVM的窑况识别方法

针对氧化铝回转窑烧成带工况变化复杂难以实现连续在线检测,长期依赖人工看火操作的难题,提出了利用计算机图像处理技术模拟传统的人工看火过程进行窑况识别研究的方法,方法包括两个部分:提取烧成带火焰图像特征,融合关键工艺过程数据组成混合特征;建立具有准正态二叉树结构的支持向量机窑况识别模型对混合特征数据进行分类识别.最后,应用该方法对采集得到的火焰图像数据与过程数据进行仿真实验研究,获得了满意的效果.