《智能交通信号灯毕业设计外文翻译.doc》由会员分享，可在线阅读，更多相关《智能交通信号灯毕业设计外文翻译.doc（8页珍藏版）》请在三一办公上搜索。
1、智能交通信号灯摘要：信号控制是一种必要的措施以确保的质量和安全,交通循环。现在的信号控制的进一步发展具有极大的潜力来减少运行时间、车辆、事故成本和整车排放。检测的发展和计算机技术改变了交通信号控制从定时开环规定自适应反馈控制。目前的自适应控制方法,像英国、瑞典MOVA SOS)和英国(孤立的信号(area-wide又控制),采用数学优化与仿真技术来调整信号波动的时间观察到的交通流实时的。优化是通过改变时间和周期长度的绿色的信号。在area-wide交叉口控制偏移是之间也发生了变化。已经开发为几种方法确定最优周期长度和最小延迟在十字路口,但基于不确定性和严格的交通信号控制的本质,全局最优是不可能
2、找到的。1引文：由于越来越多的公众意识的环境影响道路交通许多当局现在所追求的政策来:，管理供求拥挤，影响模式和路径选择;贯彻“三个代表”重要思想,提高公共汽车有轨电车和其他公共服务车辆;设施提供更好的、更安全,骑自行车和行人的道路使用者等脆弱;降低汽车排放、噪声和视觉入侵;为所有道路改善安全用户群。 在自适应交通信号控制的弹性增强的增加的数量在周期层叠的绿色阶段,从而使数学优化非常复杂和困难。因为这个原因,自适应信号控制在大多数情况下不是建立在精确的优化上,而是建立在绿色的扩展原理。在实践中,遵循的均匀性是最主要的交通信号控制安全的原因。这一规定的限制的周期时间和相位的安排。因此,在实践中是交
3、通信号控制的针对性的解决方案和调整的基础上由交通规划者。现代可编程信号控制器以大量的可调参数是非常适合这一过程。对于好的结果,一个经验丰富的策划人和微调领域中是必要的。模糊控制已经被证明是成功的,在这些问题中,精确的数学建模是困难的或不可能的,但一名有经验的人可以控制的工艺操作。因此,交通信号控制是一种适合于任务特别为模糊控制。事实上,最古老的文化之一的潜力的例子是一个模拟的模糊控制在一个inter-section交通信号控制的两个单向的街道。即使在这个非常简单的情况下,模糊控制是至少在作为一个良好的传统的自适应控制。一般而言,模糊控制是发现在复杂问题都优于用多目标决策。在交通信号控制多种交通
4、流竞争来自同一时间和空间,而且不同的优先选择往往不同交通流或车辆组。此外,优化标准,包括几个同时喜欢平均和最大车辆和行人延误、最大队列长度和百分比停止的车辆。所以,它很可能是很有竞争力的模糊控制在复杂真实的十字路口的地方传统的优化方法的使用是有问题的。2模糊逻辑：介绍了模糊逻辑,并成功地应用于大范围的自动控制任务。最大的好处模糊逻辑是有机会模型与不确定的模糊决策。此外,模糊逻辑有能力理解语言指令和控制策略的基础上产生的先验的沟通。这一点在利用模糊逻辑来控制理论的基础上,是模仿人类专家控制的知识,而不是为了构建过程本身。的确,模糊控制已经被证明是成功的,在这些问题中,精确的数学建模是困难的或不可
5、能的,但一名有经验的操作员可以控制的过程。一般而言,模糊控制是发现在复杂问题都优于多目标决策。目前,有大量的基于模糊推论系统技术。不过它们当中的主要部分,受含糊不清的根基;即使它们大都是古典数学方法表现更好,他们还带有黑色的盒子,如德模糊化,这是很难证明数学或逻辑的。例如,如果-然后模糊规则,它们在核心的模糊推理系统,经常报道的工作方式,是Ponens概括规则推理机制的经典,但随便起来就不是这样的,这之间的关系,这些规则和多值逻辑是任何已知的复杂和人工。此外,专家系统的性能应相当于人类专家:它应该得到同样的结果,专家给,但提醒当控制问题是如此模糊,专家是不确定适当的行为。现有的模糊专家系统很少
6、满足这第二种情况。然而,很多研究观察,模糊推理的方法是基于相似。Kosko,举个例子,写的模糊隶属代表的相似性定义对象特性的imprecisely。以这句话严重,我们学习系统的多值等价,即模糊相似度。原来,从Lukasiewicz多值逻辑的定义,我们能构建出一个模糊推理方法的表演,依赖于专家知识推理和只在定义的逻辑概念。所以,我们不需要任何人造的解模糊化方法确定(如重心)决定最后输出的推断。我们基本的观察是,任何的模糊集的生成一个模糊相似度,这些相似之处可以结合到一个模糊关系,变成了一个模糊相似度,太。我们把这称为诱导模糊关系总模糊相似度。如果-然后模糊推论系统实际上是选择:比较了每一个问题的
7、IF-part规则库以一实际输入值,找到最相似案例和火相应的THEN-part;如果它并非是独一无二的,使用一个标准赋予了一位专家来进行。基于多值逻辑Lukasiewicz welldefined,我们展示如何使用该方法可以正式实施。假设和原则模糊交通信号控制交通信号控制是用来最大限度地提高效率的现有交通系统。然而,交通系统的效率,甚至可以模糊。通过提供时间分离的权利的方式接近流动,交通信号产生深刻影响了效率的交通流。它们能操控的优势或者劣势的车辆和行人的;取决于权利的分配方式。因此,正确的应用、设计、安装、操作和保养维护,交通信号的关键,是安全、高效有序的交通十字路口的运动。在交通信号控制的
8、,我们都能找到某种中不确定性的许多层面。交通信号控制的输入是不准确的,而且这也意味着我们无法处理的交通方式的确切位置。可能性是复杂的控制,并处理这些可能性是一个极其复杂的任务。安全、最小化最大化,减少延迟环境方面的一些目标的控制,但这是很难处理大家聚在一起,传统的交通信号控制。causeconsequence -关系的解释也不可能在交通信号控制。这些都是典型特征的模糊控制。基于模糊逻辑控制器的设计来捕获的关键因素,而不需要控制过程中许多详细的数学公式。由于这个事实,他们有许多优势,在实时应用。有一个简单的运算的控制器结构,因为它们不需要很多的数值计算。他们的IFTHEN逻辑推理规则不需要很多的
9、计算时间。同时,控制器能够进行了大范围的输入,因为不同的控制规则可适用于他们。如果系统相关知识是为代表的IFTHEN简单模糊控制器的规则,fuzzy-based可以控制系统具有效率及减轻。的主要目标是确保交通信号控制交叉口安全系统通过保持冲突交通流分开。最优性能的十字路口相结合的系统工程,环境影响时间价值和交通安全。我们的目标是优化系统,但是我们需要来决定什么属性和重量将被用来判断最优。 整个的知识的过程中,系统设计者对交通信号控制在这种情况下,被控制的能量储存在规则知识库。有一个基本的规则从而影响系统的闭环的行为,因此它们应该是获得了彻底。规则的发展是很耗时,设计师经常需要为他翻译过程知识转
10、化为合适的规则。Sugeno提到的四种方法,推导出恶化模糊控制规则:1.运营商经验2:控制工程师的知识2,3,6,7,11,143;该主算子的来讲模糊建模的控制措施4.模糊建模的过程5.酥脆的建模过程6;髓启发式的设计规则7;往往在线改编的规则。通常一个组合这些现象的一些方法是必要的,以获得较好效果。在常规控制经验,增加设计的模糊控制器,导致减少开发时间。项目的主要目标是FUSICO-research理论分析的模糊交通信号控制,广义模糊规则的交通信号控制使用语言变量,验证了模糊控制原理和校准的隶属度函数,并发展了一种模糊自适应信号控制器。vehicle-actuated控制的策略,如SOS,M
11、OVA和LHOVRA是控制算法,对第一代。模糊控制算法,该算法可以之一的第二代,代的人工智能(AI)。摘要模糊控制是有能力处理多目标的、多维的和复杂的交通状况,如交通信号。模糊控制的典型优点是简单的流程,有效控制,提高产品质量。3 FUSICO：FUSICO-project塑造出的经验的警察。这个规则库的发展是在1996年秋季。j . they Kari正常,经验丰富的交通信号规划师,工作时在赫尔辛基理工大学在这个时间。每天工作小组讨论,他的经验帮助我们模型对我们的规则。在特定情况下病理交通拥堵或很少有车辆在循环;在那里first-in-first-out是唯一合理的控制策略。该算法寻找最相似
12、的实际IF-part输入值,并给出了相应的THEN-part然后被解雇了。交通信号控制系统三个现实的方法来构造算法和仿真模型检验他们的表现。要解决问题,类似的仿真Mamdani non-fuzzy和古典风格的模糊推理系统,也是。结果对车辆和行人延误或平均平均车辆延误,在大多数情况下更好的在模糊相似度为基础的控制比在其他的控制系统。比较模糊相似度为基础的模糊控制的控制和Mamdani风格也强度的假定,在近似推理过程中时,一个基本概念是多值之间的相似的对象,而不是一种概括规则的推理方式,Ponens经典。FUSICO项目结果这个计画的结果表明,模糊信号控制的潜力是孤立交叉口控制的一种方法。比较结果
13、的Pappis-Mamdani控制、模糊孤立的人行过街和模糊两阶段的控制是很不错的。结果表明,孤立的人行过街的模糊控制提供了有效的两种对立的目标妥协,最低行人延误和最小的车辆的延误。结果对两相控制和Pappis-Mamdani控制表明,模糊控制应用领域很广。改进的最大延时超过20%,这意味着模糊控制的效率可以比传统的vehicle-actuated控制的效率。根据这些结果,我们可以说,模糊信号控制可以多目标和更有效率,比常规自适应信号控制现在。最大的好处,或许,达到更复杂的十字路口和环境。这FUSICO-project仍在继续。目的是将一步步的更复杂的交通信号,并继续对模糊控制理论著作。第一个
14、例子将公共交通优先考虑的问题。 原文： Intelligent traffic lightsAbstract：Signal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The dev
15、elopment of detection and computer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOVA, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimiza
16、tion and simulation techniques to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have be
17、en developed for determining the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.1.Citation：As a result of growing public awareness of the environmental impact of road t
18、raffic many authorities are now pursuing policies to: manage demand and congestion; influence mode and route choice; improve priority for buses, trams and other public service vehicles; provide better and safer facilities for pedestrians, cyclists and other vulnerable road users; reduce vehicle emis
19、sions, noise and visual intrusion; and improve safety for all road user groups.In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the
20、adaptive signal control in most cases is not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal contr
21、ol in practice are based on tailor-made solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fu
22、zzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potential
23、s of fuzzy control is a simulation of traffic signal control in an inter-section of two one-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective
24、 decisions. In traffic signal control several traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedest
25、rian delays, maximum queue lengths and percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic.Fuzzy logic has been introduced and successfully applied to a wide ra
26、nge of automatic control tasks. The main benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in
27、 utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control
28、process. In general, fuzzy control is found to be superior in complex problems with multi-objective decisions.At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classica
29、l mathematical method, they still contain black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule
30、 of inference, but literally this not the case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn
31、when the control situation is so vague that an expert is not sure about the right action. The existing fuzzy expert systems very seldom fulfil this latter condition.Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes Fuzzy membership.represents s
32、imilarities of objects to imprecisely defined properties. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasonin
33、g such that the inference relies only on experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy simil
34、arity, and that these similarities can be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual inpu
35、t value, find the most similar case and fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.Hypothesis and Principles of Fuzzy Traffic Signal C
36、ontrol Traffic signal control is used to maximize the efficiency of the existing traffic systems 6. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of tra
37、ffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movemen
38、t of traffic at intersections.In traffic signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these p
39、ossibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible
40、to explain in traffic signal control. These are typical features of fuzzy control.Fuzzy logic based controllers are designed to capture the key factors for controlling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applicatio
41、ns. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules ca
42、n be applied to them. If the system related knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apar
43、t. The optimal performance of the signalized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.The entire knowledge of the system designer about
44、the process, traffic signal control in this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basic influence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often h
45、ave to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:1. operators experience2. control engineers knowledge3. fuzzy modelling of the operators control actions4. fuzzy modelling of the process5. crisp modeling of the process6.
46、heuristic design rules7. on-line adaptation of the rules.Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.3. FUSICOThe main goals of FUSICO-res
47、earch project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-ac
48、tuated control strategies, like SOS, MOVA and LHOVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be one of the algorithms of the second generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi
49、-dimensional and complicated traffic situations, like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyda
