交通基础设施建设投资对国民经济拉动作用的定量分析

运用投入产出模型定量分析了公路交通基础设施建设投资对国民经济的拉动作用。拉动作用主要表现为公路建设投资所产生的直接、间接和诱导经济效益,其总和为公路建设投资的乘数效应,可分别用产出乘数、GDP乘数和就业乘数来测算。分析测算了我国近年来公路建设投资对国民经济的拉动作用。估算结果表明,(1)公路建筑业对其他部门的波及影响程度大于各部门的平均影响水平;(2)拉动作用在1998年最为显著,自1999年开始,公路投资拉动经济增长的百分比呈逐年上升的趋势。     

A multiple discrete-continuous model for independent- and joint-discretionary-activity participation decisions

This paper presents a multiple discrete-continuous econometric structure to model the daily time-investment decisions of couples in solo- and joint-discretionary activities incorporating intra-personal and inter-personal inter-dependencies. The empirical model was estimated using data from the 2000 Bay Area Travel Survey. The results indicate evidence of the positive impact of vehicle availability on independent activity participation and the negative impacts of the presence of children and mandatory time investments on the joint discretionary-activity engagement of the spouses. In addition, we also find the mandatory- and maintenance-activity-participation characteristics of the spouse to influence the discretionary activity choices of individuals. Finally, the analysis also indicates a strong impact of common unobserved factors on the decisions of couples. From a policy analysis perspective, these results imply that demand-management actions directly impacting one adult could also result in changes to the activity patterns of his/her spouse and to changes in joint activity participation characteristics. Dr. Sivaramakrishnan Srinivasan is an Assistant Professor in the Department of Civil and Coastal Engineering at the University of Florida. His research interests include travel-behavior analysis, activity-based travel-demand modeling, and the application of advanced econometric methods for transportation problems. Dr. Chandra R. Bhat has contributed toward the development of advanced econometric techniques for travel behavior analysis, in recognition of which he received the 2004 Walter L. Huber Award and the 2005 James Laurie Prize from the American Society of Civil Engineers (ASCE).     

Analysing Demand for Suburban Trips: A Mixed RP/SP Model with Latent Variables and Interaction Effects

We analyse mode choice behaviour for suburban trips in the Grand Canary island using mixed revealed preference (RP)/stated preference (SP) information. The SP choice experiment allowed for interactions among the main policy variables: travel cost, travel time and frequency, and also to test the influence of latent variables such as comfort. It also led to discuss additional requirements on the size and sign of the estimated model parameters, to assess model quality when interactions are present. The RP survey produced data on actual trip behaviour and was used to adapt the SP choice experiment. During the specification searches we detected the presence of income effect and were able to derive willingness-to-pay measures, such as the subjective value of time, which varied among individuals. We also studied the systematic heterogeneity in individual tastes through the specification of models allowing for interactions between level-of-service and socio-economic variables. We concluded examining the sensitivity of travellers’ behaviour to various policy scenarios. In particular, it seems that contrary to political opinion, in a crowded island policies penalising the use of the private car seem to have a far greater impact in terms of bus patronage than policies implying direct improvements to the public transport service.     

基于模糊逻辑的电动汽车制动能量回馈系统

分析了电动汽车制动能量回馈的特点，针对电动汽车制动能量回馈时强鲁棒性的需求，设计了一种基于Sugeno模糊逻辑的制动能量回馈系统，以满足能量回馈的要求，该回馈系统提高了整车的制动性能以及续驶里程，也使整车的动力性、安全性和舒适性达到较好的平衡，文章同时估算了这种控制策略的能量回收效率。经仿真和实际测试，结果表明所提策略满足总体设计的性能指标要求。     

车辆主动悬架的神经网络模糊控制

利用神经网络来实现在车辆主动悬架中的模糊控制，通过对主动悬架实体装置的台架实验研究，在不同激励信号的作用下，神经网络模糊控制器都具有很好地抑制车体振动的特点，其控制效果明显优于PID控制。在车辆主动悬架中，神经网络模糊控制具有设计新颖，实用性强，特别对于减振效果要求较高的车辆，更能发挥其优点。     

具有地面不平度预测量系统的主动悬架系统的连续模糊控制

本文采用连续模糊控制方法来实现具有地面不平度预测量系统的主动悬架动力装置的控制，并通过模拟计算证明了采用此方法可使汽车和行驶平顺性和行驶安全性同时得到有效改善，结果还明，它可以有效降低主动系统消耗的最大功率和一定程度降低总能量的消耗。     

船舶舱室布置方案的模糊综合评价

在建立某型船指挥舱室布置方案评价因素集的基础上,用层次分析法确定了各因素的权重系数,采用语言值标尺及基于重心的方法构造模糊评价矩阵,最后用广义空间距离方法进行方案的比较与排序,完成某型船指挥舱室布置方案的模糊综合评价．     

高阶CMAC神经网络及其在水下机器人运动控制中的应用

对高阶ＣＭＡＣ神经网络的结构和工作原理进行了研究,提出了中间层作用函数地址的计算方法,给出了计算高阶基函数的不同方法,利用高阶ＭＣＡＣ神经网络对水下机器人模糊深度控制器进行了学习,仿真结果显示了不同高阶基函数ＭＣＡＣ网络的不同建模能力,证明了中间层作用函数地址的计算方法正确。     

散装货船模糊综合安全性评估的实现方法

基于模糊数学的理论,将模糊多级综合评判方法运用于散装货船的综合性评估。通过综合分析与模糊化处理影响散装货船安全笥的船舶、船员及环境三方面的各种因素,建立了模糊综合安全性评估的数学库和计算机程序。     

一种水下目标识别方法──基于模糊理论的多种神经网络融合分类

在应用多种方法提取目标噪声中分类特征的研究基础上,本文设计出基于模糊理论的多神经网络融合分类器,其研究重点主要包括有监督分类的模糊聚类神经网络分类、多神经网络与模糊综合分析相结合的融合分类。基于大量海试实测信号样本的多组实验表明,该分类方法具有良好的工程应用前景。