An Adaptive Neuro-Fuzzy Inference System for estimating the number of vehicles for queue management at signalized intersections

Queue management is a valuable but underutilized technique which could be used to minimize the negative impacts of queues during oversaturated traffic conditions. One of the main obstacles of applying queue management techniques along signalized arterials is the unavailability of a robust and sufficiently accurate method for measuring the number of vehicles approaching a signalized intersection. The method based on counting vehicles as they enter and exit a specific detection zone with check-in and check-out detectors is unreliable because of the likely systematic under or over counting and the resulting cumulative errors. This paper describes the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in the development of a new fuzzy logic-based approach for estimating the Number of Vehicles in a Detection Zone (NVDZ) by using detector time-occupancy data (instead of detector counts). Microscopic simulation results are used to evaluate the accuracy of the NVDZ estimates. Tests were carried out to determine the transferability of a tuned Fuzzy Inference System (FIS) and to check the sensitivity of the calibrated FIS to detection coverage, the location of the detection zone relative to the signalized (bottleneck) intersection, the length of the detection zone, and different signal timings at the bottleneck intersection. Results show that the NVDZ estimation based on fuzzy logic seems to be a feasible approach. Although the primary objective of developing the NVDZ estimation technique has been queue management, other applications such as ramp metering and incident detection could potentially use the same technique.     

Fuzzy-logic applied to yaw moment control for vehicle stability

In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).     

基于模糊神经网络的筑路机械生产率预测方法

以高等级公路沥青混凝土面层施工机械系统为研究对象，提出了一种施工机械生产率的预测方法，该方法利用神经网络和模糊数学，通过鉴定和收集相关数据，设计了带有模糊输入出系统的神经网络预测模型，该预测方法与现有的统计预测模型相比，使用更加简便，预测结果的准确性更高，该模型是针对沥青路面施工机械提出的，其建模的思想和方法对其他土木工程机械化施工系统也有一定的借鉴意义。     

基于串联型模糊神经网络的汽车半主动悬架的研究

本文建立了五自由度汽车半主动悬架系统模型，提出一种用于汽车悬 半主动振动控制系统的模糊神经网络方法，对半主动悬架 计算机仿真和结果分析，并通过与被动悬架相比较，证明半主动悬架系统在减少振动，提高汽车平一方面要优于被动悬架。     

可调螺距螺旋桨模糊控制系统

介绍了可调螺距螺旋桨系统特点及其仿真数学模型,模糊控制规则的确定。仿真研究表明模糊控制比常规ＰＩＤ控制具有突出的优点。     

散装货船模糊综合安全性评估的实现方法(二) --船员因素的组成与作用分析

文章继续对散装货船模型综合安全性评估方法中船员因素的组成、结构及包中内容与作用,进行了较为详细的分析与讨论,目的是通过模糊量化的方法,说明散货船的安全必瑟船员系统安全因素的关系。文章并用事例说明了该方法对散货船综合安全性主送的科学恶性循环与客观性。     

模糊罚函数遗传算法在船舶型线扫描图光顺中的应用

采用遗传算法进行优化时,处理约束条件的方法一直是理论发展的重点。文章将模糊理论用于优化中处理约束条件的罚函数方法,提出了一种处理的约束优化问题的模糊罚函数遗传算法,并将这种方法用于船舶型线扫描光栅型线的光顺问题,得到了很好的结果。     

汽车液压主动悬架系统模糊控制研究

设计了一个汽车液压主动悬架装置，运用模糊控制技术对所设计的悬架系统的控制策略及控掣登嚣进行了研究。研究结果表明，所设计的主动悬架装置是正确的，采用模糊控制策略能够保证悬架系统具有较好的控制效果。     

金属带式无级变速器速比控制的试验研究

基于力-位置控制方案开发了金属带式无级变速器的控制测试系统：在无级变速器的控制系统中，考虑到速比控制阀的饱和非线性特性和无级变速系统的复杂非线性特性，设计了基于速比反馈的速比模糊控制器：试验结果表明，所设计的液压控制系统是可行和实用的。     

汽车倒车危险度评判基本模型初探

基于模糊数学理论和碰撞危险度概念，通过引入最小停车距离，综合考虑倒车速度、车辆与障碍物之间的距离、驾驶员生理心理因素、路面附着系数对倒车安全的影响，建立了分层次危险度评判公式，提出一种新的倒车时危险度评判方法。