马鞍山特长高速公路隧道通风控制系统研究

运用前馈式神经网络理论、空气动力学理论以及模糊控制理论,针对马鞍山特长隧道的特点,以污染物浓度预测增量、污染物实测浓度和控制目标为输入,以需要增加或减少的风机组数为输出,构建了前馈式智能模糊控制系统,达到了隧道按需通风、节能减排的目的,解决了传统后馈式通风控制方式滞后、粗放的问题,利用Matlab软件对该系统进行仿真、调试,验证了该系统的有效性,通过计算马鞍山隧道一天开启风机的耗电量可知,采用前馈式智能模糊控制系统可以比采用传统后馈式通风控制系统相对节能12.21%左右,节能减排效果显著,对类似隧道工程的研究具有参考和指导意义。     

城市道路网络优化设计方法初探

城市道路是城市交通的载体之一,是城市交通系统的重要组成部分,直接决定着城市发展目标能否实现。本文通过国内外对城市道路研究的比较,在分析我国城市道路网络存在的问题基础上,对城市道路网络的优化方法进行探讨,并提出优化道路网络功能的思考与建议。     

基于GIS的公路安全信息管理系统研究及应用

利用GIS技术手段建立了唐山市道路信息电子数据库和道路网络,在Visual Stidio2008平台基础上,进行Web GIS唐山市公路安全信息管理系统的开发,实现了集城市公路安全评价决策分析、交通事故影响范围缓冲分析、交通事故紧急救援最佳路径分析等功能于一体的数字化公路安全信息管理系统。     

神经网络法负荷预测与蓄冰空调系统的运行优化

文中介绍了蓄冰空调系统几种常见的控制策略。提出蓄冰空调系统的运行优化必须进行准确的负荷预测,并给出采用神经网络模型(ANN) 预测负荷的方法     

相空间重构与RBF结合的短时交通流预测研究

针对短时交通流的混沌特性,提出将相空间重构与神经网络相结合的预测算法,并采用某高速公路实时交通流数据进行了仿真验证,取得了较好的预测效果。     

基于优化神经网络模型的隧道变形预测研究

神经网络预测技术一直是隧道学术界和工程界关注的关键课题.传统的神经网络对初始权值的依赖性很大,不同的初始值会导致差异很大的预测结果,并且初始值选择有很大的随机性和盲目性,往往导致网络振荡或不收敛.根据经典遗传算法的优势,对神经网络初始值进行优化,完成PB训练样本并建立非线性预测模型,避免了神经网络对初始权值的依赖性过大而引起计算误差.研究表明,GA-BP神经网络遗传算法适用于预测高速公路隧道断层破碎带围岩变形量,与现场试验数据相吻合,验证了GA-BP神经网络遗传算法工程应用的可行性,提高了预测的精度及避免人为误差.隧道洞周变形主要集中在开挖后12天,在此时间内应加强监控量测频率,以避免隧道过大变形引起坍方事故.     

Traffic Volume Time-Series Analysis According to the Type of Road Use

Problems related to highway traffic operation and congestion management can be alleviated with the use of modern intelligent transportation systems (ITSs). Advanced Traveler Information Systems (ATIS) is one of the emerging technologies that will help travelers plan routes and schedules of their trips so as to redistribute the traffic over the highway network. Such redistribution will try to maximize the use of available highway capacity. Collections of real-time data and short-term predictions of traffic volumes are among the critical needs of an ATIS. This article studies characteristics of different traffic volume time series. In particular, time-series analysis is applied to the prediction of daily traffic volumes. The daily traffic volume is estimated by using the previous 13 daily traffic volumes. The study involves a comparison of statistical and neural network techniques for time series analysis. The analysis is applied to different types of road groups according to the trip purpose and trip length distribution. It is hoped that this study will provide a better understanding of various issues involved in the short-term prediction of traffic volumes on different types of highways.     

Nonlinear Identification of Dynamic Systems Using Neural Networks

A neural-network-based method is proposed for the modeling and identification of a discrete-time nonlinear hysteretic system during strong earthquake motion. The learning or modeling capability of multilayer neural networks is explained from the mathematical point of view. The main idea of the proposed neural approach is explained, and it is shown that a multilayer neural network is a general type of NARMAX model and is suitable for the extreme nonlinear input-output mapping problems. Numerical simulation of a three-story building and a real structure (a bridge in Taiwan) subjected to several recorded earthquakes are used here to demonstrate the proposed method. The results illustrate that the neural network approach is a reliable and feasible method.     

公路建设项目交通量的预测方法研究

分析了传统公路建设项目交通量预测方法存在的主要问题,从综合运输的角度出发,提出了基于综合运输网络的公路建设项目交通量预测方法,并对预测方法涉及的具体模型进行了讨论,认为应用该方法可以客观反映公路与其他运输方式之间的作用关系、准确描述交通量的转移规律、充分把握建设项目在综合运输网络中的作用和地位,使预测结果更加合理、有效。     

An Approach to Dynamical Classification of Daily Traffic Patterns

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.     

Modelling of structural response and optimization of structural control system using neural network and genetic algorithm

This paper proposes an integrated approach to the modelling and optimization of structural control systems in tall buildings. In this approach, an artificial neural network is applied to model the structural dynamic responses of tall buildings subjected to strong earthquakes, and a genetic algorithm is used to optimize the design problem of structural control systems, which constitutes a mixed‐discrete, nonlinear and multi‐modal optimization problem. The neural network model of the structural dynamic response analysis is included in the genetic algorithm and is used as a module of the structural analysis to estimate the dynamic responses of tall buildings. A numerical example is presented in which the general regression neural network is used to model the structural response analysis. The modelling method, procedure and the numerical results are discussed. Two Los Angeles earthquake records are adopted as earthquake excitations. Copyright © 2000 John Wiley & Sons, Ltd.     

基于生境网络构建优化的生物多样性保护 规划途径研究

城市绿色空间是承载生物多样性的关键,以其为基 础构建生境网络有助于整合破碎绿地斑块,促进物种交流与迁 徙,为生物多样性保护提供基础。现有生境网络构建过程中存 在依赖单一模型、忽略区域实际物种情况、网络构建的工作路 径尚未统一、输出成果难以与上位规划衔接并在规划层面进行 实践应用等问题。在明确生物多样性保护规划工作内容与目标 的基础上,综合运用生境质量分析、地理探测器、最小累积阻 力模型等模型方法,识别区域生境源地和廊道,并在考虑区域 指示物种的基础上补充重要生境节点,构建面向生物多样性保 护的生境网络,系统优化了“源地识别-廊道模拟-节点提取- 网络构建”的网络构建方法;以对接国土空间规划下的绿地系 统专项规划为目标,将生境网络构建结果通过红线比对、绿地 分类匹配等途径,从空间管控角度提出保护措施,实现与绿地 系统规划的有效衔接,构建统一规范的生物多样性保护规划工 作路径,以通城县为例验证工作路径的可行性,优化面向生物 多样性保护的生境网络构建方法,为县域绿地系统规划与生物 多样性保护规划提供科学依据。     

基于机非分流的大城市自行车路网规划研究

为解决"机非混行"带来的交通问题和保障自行车交通的合理发展,通过对以往规划和实践的反思,基于"机非分流"的思路,提出应用分区分级的规划方法开展大城市自行车路网规划的研究,并对分区的划分和交通策略进行了探讨。通过与传统自行车路网规划方法的比较,提出按功能分类将自行车道路分为四级:市级自行车通道、区级自行车干道、区内自行车集散道和绿色自行车休闲道;重新定位自行车各级道路功能,并分析各级路网上的路权分配、相应的指标设置。在构建独立的自行车路网的同时,重点考虑了自行车路网规划对城市路网规划与老城交通改善的要求。     

论上海城市外围区域布局调整和交通系统的协调发展——以高速公路网规划为例（上）

根据上海城市布局调整和交通系统服务需求,按照与城市发展目标相适应的交通系统规划理念形态和规划目标,提出了上海高速公路网规划与城镇体系的配合,以及当前郊区交通系统规划中存在的问题.     

Automated Path Planning for Mobile Crane Lifts

Planning the lift path of a crane is an important subtask within the heavy–lift planning process. This paper reports the work done toward applying configuration space (C–space) and search concepts to develop a tool to identify lift paths that satisfies the planning requirements. The C–space is generated using an interference detection technique. Two levels of heuristic search are performed within the C–space. The first search is a heuristic depth search to determine the obstacle–free lift paths. The second search performs a more detailed optimization of the path within a constrained search space. The tool can be used within the AutoCad environment and is based on program modules developed using AutoLisp and external programs. The system was tested on a number of problems. It was found that the system was capable of generating good paths in complex situations.     

深圳大学学府医院交通规划分析与评价

本文通过对深圳大学学府医院基地本身和外围交通状况的研究,从宏观和微观两个层面进行剖析,从交通规划方案研究的必要性、特点及总体要求、交通规划目标及原则、交通系统优化、规划构想以及交通规划终极目标——人性化关怀等方面进行阐述大型三甲医院设计中交通问题的方方面面,从而提出目前大型医院停车难,交通拥堵的方法及对策。     

基于小生境等维BP神经网络的沉降预报

针对传统BP神经网络全局优化能力低、无法学习的缺陷,引入遗传算法中的小生境技术,研究了基于小生境等维BP神经网络模型,同时利用MATLAB进行编程实现。该模型的核心思想是借助小生境遗传算法优化神经网络的连接权和阈值,进而提高了等维BP神经网络模型的全局优化能力,改善了模型的收敛性。结合宁波某大楼沉降监测实例,利用小生境等维BP神经网络、GM(1,1)模型、等维BP神经网络模型分别对沉降数据建模预测,结果表明,小生境等维BP神经网络模型更加符合实际情况、预测效果更佳。     

基于神经网络的交通方式划分模型研究

在分析交通方式划分预测的一些基本理论的基础上,尝试应用概率神经网络进行交通方式划分建模,并给出了算例,分析表明该模型对交通方式划分问题不仅有很强的解释性,同时具有很好的可操作性.     

Dynamic Wavelet Neural Network for Nonlinear Identification of Highrise Buildings

Abstract:   Recently, the authors presented a multiparadigm dynamic time-delay fuzzy wavelet neural network (WNN) model for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs. Compared with conventional neural networks, training of a dynamic neural network for system identification of large-scale structures is substantially more complicated and time consuming because both input and output of the network are not single valued but involve thousands of time steps. In this article, an adaptive Levenberg–Marquardt least-squares algorithm with a backtracking inexact linear search scheme is presented for training of the dynamic fuzzy WNN model. The approach avoids the second-order differentiation required in the Gauss–Newton algorithm and overcomes the numerical instabilities encountered in the steepest descent algorithm with improved learning convergence rate and high computational efficiency. The model is applied to two highrise moment-resisting building structures, taking into account their geometric nonlinearities. Validation results demonstrate that the new methodology provides an efficient and accurate tool for nonlinear system identification of high-rising buildings.     

软土地基沉降预测的一种智能方法

常规方法对软土地基沉降的估计存在一定的问题。利用一种新的智能方法来预测软土地基的沉降:通过已经观测到的沉降数据,建立了基于改进观测数据的神经网络预测模型,依此提出了基于部分实测沉降数据来预测短期沉降及最终沉降的方法。算例表明:对高度复杂非线性的土工结构问题,神经网络能够较为精确的解决,而且操作简单、泛化能力强。