论《罗伯特议事规则》在公共事业管理课程实训教学中的应用

文章立足于公共事业管理专业培养目标和实践教学的特点,分析了在公共事业管理专业开展《罗氏议事规则》实务训练的选题背景和必要性,论述了《罗氏规则》实训的设计方案及具体操作流程,总结了实训工作取得的成果,提出了增强实务训练效能的技巧和应注意的相关问题。     

The limits of path error-neglecting in straight lane driving

Abstract

It is argued that driving cannot simply be considered as a permanent, closed-loop task. Time-to-line-crossing (TLC) is used as a measure to quantify the potential role of visual open-loop and path-error-neglecting strategies. Basically, TLC represents the time available for a driver to neglect path errors until the moment at which any part of the vehicle reaches one of the lane boundaries. The strategy adopted by drivers during error-neglecting should be represented in terms of decision rules, describing how drivers switch from error-neglecting to error-correcting when approaching the edge of a lane. The experiment to be presented in this paper was designed to provide these rules for a straight lane-keeping task. Drivers were instructed to neglect the vehicle path error and to switch to error-correcting only at that moment when the vehicle heading could still comfortably be corrected to prevent a crossing of the lane boundary. The results show that the lateral distance from the lane boundary at which drivers switch to error-correction increases about linearly with the lateral approach speed. This mechanism results in an approximately constant TLC (time) distance at the moment of decision: this result being consistent over a broad range of speeds.     

WTO规则下我国农民增收途径探析

根据世贸组织关于农业的规则和中国政府的承诺，我国农民增收出现了价格保护终结，农作物生产成本过高，农民成本外支出过大等不利因素，应采取调整种植结构、加快城市化步代、健全农业保护政策、加强农业科技交流与合作等对策以增加农民收入。     

一种新兴的数据挖掘方法:神经规则法

该文详细阐述了一种新兴的数据挖掘方法——神经规则法。文中先后介绍了神经网络的训练、神经网络的剪枝以及神经规则的提取。最后用一个实例具体解释了使用该方法来挖掘分类规则的过程。     

自适应模糊神经控制器在MIMO 系统中的应用研究

首先给出一种适用于MIMO系统的自适应模糊控制器，然后针对该控制器用于复杂系统时，存在模期规则过多且建立规则的时间随规则数增加呈指数增长的问题，提出了另一种适用于MIMO非线性系统的自适应模期神经控制器，该控制器采用“全逼近”的控制策略，依据李亚普诺夫方法给出了模糊神经自适应输出反馈控制律和参数自适应律，仿真研究证明了MIMO非线性系统系统的稳定性以及跟踪误差的收敛性。     

一种新的多维稀疏规则模糊插值推理方法

经典的插值理论针对一维稀疏规则库的条件，提出了各种不同的插值方法，取得了很多很好的经验．但对多维稀疏规则条件的近似推理，研究很少，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题．为了在多维稀疏规则条件下能得到好的插值推理结果。提出了一种基于几何相似的插值推理方法．该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具．     

产品品牌与标准化

简要阐述了品牌与标准化的关系,指出了企业要创品牌首先要抓标准,要重视国际先进标准的转化和采纳,企业要成为标准化工作的主体.     

试论WTO与我国高等教育的发展观

加入WTO后,中国高等教育面临更加复杂的环境,机遇多于挑战。面对中外不同办学思想和模式的冲突及教育服务贸易竞争日益激烈的形势,必须树立科学的发展观,不断调整应对策略,使WTO中的中国高等教育与时俱进,为科教兴国战略的实施作出应有的贡献。     

Mixture equations of state: Composition dependence

This paper discusses theoretical models for the composition dependence of equations of state and compares the quality of predictions against experimental thermodynamic property data. The mean density approximation (MDA) and the van der Waals one-fluid (VDW1) model are compared with hybrid mixing rules (HMR), in which rigorous composition dependence is used for the second and third virial coefficients and the conformai solution model is used for equation-of-state density terms beyond the third virial term. It is found that when values of unity are used for all binary and three-body unlike interaction parameters, calculated densities for methane-normal heptane mixtures have average absolute deviations of 3.54% for MDA, 4.04% for VDW1, and 2.59% for HMR. When vapor-liquid equilibrium calculations were performed for the methane-normal heptane system, average absolute deviations of calculatedK values from experimental values were 16.7% for methane and 36.4% for normal heptane using HMR, whereas when conformal solution model (CSM) mixing rules were used, the results were 34.8% for methane and 66.7% for normal heptane. When the binary interaction parameter for the characterization of interaction energies is determined, it is found to be less sensitive to state conditions in the case of HMR than either MDA or VDW1. These preliminary results suggest the potential of mixture equation-of-state methods which utilize rigorous composition dependence for the lower-order virial coefficients.Paper presented at the Second U.S.-Japan Joint Seminar on Thermophysical Properties, June 23, 1988, Gaithersburg, Maryland, U.S.A.     

基于Rough集方法的RS-MAXNET网络分类器模型

Rough集（RS）理论中如何补偿或拓展Rs约简的局部决策规则使之更加接近全局规则是一个重要的议题。本文提出RS与MAXNET网络结合拓展建立盼MAXNET全局网络分类器模型。基于上述模型进行的脱机手写模式识别算法说明了其有效性。