基于模糊C均值聚类和神经网络的短时交通流预测方法

短时交通流预测是动态交通控制和诱导的前提。提出一种模糊C均值聚类和神经网络相结合的短时交通流预测方法。用同一组实测数据对比计算了该方法与BP神经网络预测方法、模糊神经网络预测方法分别得到的预测结果。计算结果表明：所提出的方法的预测准确性明显地高于其他两种方法。     

Windows NT下视频传输卡的多卡驱动程序开发方法

由于WindowsNT操作系统不允许应用程序直接操纵硬件,从而给开发与硬件结合的软件项目带来了一定的困难,因而如何进行硬件驱动程序的编写就成了Windows环境下一个重要的程序开发领域。给出了WindowsNT系统下基于PCI总线的硬件设备驱动程序开发的一般方法,以及如何运用SoftICE来进行驱动程序跟踪调试的一般方法,最后给出了一种同时支持多块视频传输卡的多卡驱动程序的开发方法。     

DECISION MAKING - THE ANALYTIC HIERARCHY AND NETWORK PROCESSES (AHP/ANP)

This is the first part of an introduction to multicriteria decision making using the Analytic Hierarchy Process (AHP) and its generalization, the Analytic Network Process (ANP). The discussion involves individual and group decisions both with the independence of the criteria from the alternatives as in the AHP and also with dependence and feedback in the entire decision structure as in the ANP. This part explains the Analytic Hierarchy Process, with examples, and presents in some detail the mathematical foundations. An exposition of the Analytic Network Process and its applications will appear in later issues of this journal.     

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.     

一种基于流量工程的网络端到端性能分析算法

网络性能分析与优化是面向流量工程网络设计与管理的关键任务之一。以流量工程为基础,应用网络图的方法描述网络拓扑,通过对链路性能参数描述矩阵的标准化变换及网络邻接关联矩阵传递闭包的求解,同时结合网络事件的随机性,给出一种求解网络链路数据传输能力、分析网络端到端连通性能的算法,为网络设计及其性能分析提供了一种便捷可行的方法。     

中小企业网络组织的规模经济分析

中小企业生存和发展的特点决定其网络组织具有整体目的长期化，企业功能分布式集成，项目导向的构想和实施的即时化等特点,由于信息技术和制造技术的进步，中小企业可通过适合的网络组织整合和利用外资源，取得大企业同样的规模经济效益，弥补其由于规模小而带来的市场，技术创新，抵抗风险和复杂任务解决等能力的不足。     

基于MATLAB的神经网络在股市预测中的应用

以道氏三大假说作为理论基础，设计一个三层BP网络，并建立相应的数学模型，以MATLAB为工具，个股上海石化(600688)140天实际收盘价作为样本，对网络进行训练后，预剩10天的收盘价，并计算出与实际收盘价的高差Q＝0．0146，Q已经小到几乎可以忽视的程度。     

考虑施工次序与土质问题的土石方调运的网络模型研究

土石方施工在高速公路建设中数量巨大，传统的人工调配土石方方法既费时又无法得到最优调运方案．在现有的研究基础上提出考虑施工次序与土质问题的土石方调运研究的网络模型。     

基于RBF的机械手建模误差补偿自适应控制

针对机械手动力学建模误差,提出了基于RBF神经网络误差补偿的自适应控制策略。在基于逆动力学的计算力矩控制方法的基础上,对系统输入与目标轨迹进行修正,设计了两种误差补偿自适应控制器。利用RBF神经网络对修正项在线自学习,并根据Lyapunov稳定性理论建立了网络权重自适应学习律,保证了跟踪误差的收敛及系统的稳定。以平面转动双臂机械手轨迹跟踪为例进行仿真,结果表明该方法能够有效地补偿建模误差,提高了系统的控制性能并使控制系统具有对参数摄动的鲁棒性,对于机械手自适应控制具有一定的可行性。     

液压型风力发电机组并网冲击仿真研究

以液压型风力发电机组励磁同步发电机系统和并网控制系统为研究对象,针对励磁同步发电机的准同期并网条件,建立了同步发电机和励磁系统数学模型。理论分析了同步发电机并网冲击电流和冲击转矩。通过MATLAB/Simulink建立同步发电机、励磁系统和准同期锁相模块仿真模型,采用AMESim软件建立液压调速系统模型,采用联合仿真的方法,对液压型风力发电机组准同期并网过程进行研究,分析了系统并网冲击特性。在实验室搭建30kVA实验台,实验验证了仿真模型和仿真结果的正确性。研究表明定量泵-变量马达液压调速系统能将同步发电机转速稳定控制在准同期并网条件范围内,同时能有效控制系统并网冲击,使风力发电机组平稳并入电网。