具有车流量检测功能的智能交通灯设计外文翻译

1、浙江师范大学本科毕业设计(论文)外文翻译译文：设计和施工的具有油井G模糊逻辑控制系统的智能红绿灯AIP会议论文集2008年10月7日摘要车载旅行的增加遍布世界各地,尤其是在大型的都会区域中。因此,生活中往往有很多需要交通灯的地方是需要进行模拟和优化交通控制算法,因此提出了一种更好的适应这种交通控制器方案。本文介绍了一种利用单片机控制的智能交通的模拟光控制器，该系统采用模糊逻辑是用来改变交通信号周期自适应地在一个双向的十字路口进行红绿控制。文章试图通过设计一个智能的交通灯控制系统,如PIC 16F84A微控制器和PIC 16F877A微控制器。然后交通信号可以控制的密度取决于背后的双向绿色和红色

2、的灯光。关键词模糊逻辑；智能交通信号灯控制系统；单片机；智能的基础设施；交通研究1 简介运输的研究目标是以优化交通流的人员和货物为基础。随着交流量数量的不断增加,道路使用者由现行的基础设施和提供的资源是有限的,未来智能控制的交通将成为一个非常重要的问题。然而,一些限制的使用智能交通控制的问题依然存在。例如避免交通拥堵被认为是对双方都有利的环境和经济，但也可能改善交通流负载需求增加。本文讨论了交通管制策略,这就决定了模糊逻辑控制器的设计标准。简要讨论的要素及模糊控制器的模糊化，模糊规则库由人类专家，模糊推理机的模糊模型为基础制定，并利用去模糊的特点，使摆在交通流微控制器上由模糊控制器调节仿真。微

3、控制器仿真结果表明该模糊性能优越控制器是一个传统的控制器，其信号周期可预置。 本文的主要目标是提高其安全性,减少出行时间、增加基础设施的能力。这种改进的交通控制系统有益健康、经济、环境,这个智能交通系统可分配预算。本文的优化、交通流的变化是非常重要的,从而有效减少平均旅行(或者等待)的时间。交通负荷是个高度依赖参数的系统，它有很多参数，如时间、日、季节、天气和一些不可预知的情况,比如事故,特殊事件或施工活动。如果这些参数不能考虑交通控制系统,将创造瓶颈和延迟。交通管制系统，遥感和解决不断调整交通根据实际交通负荷灯时间被称为智能交通控制系统中的瓶颈和延迟。交通流在一个交叉口是一个复杂的随机过程。

4、试图制定出一个优化判断的交通管制往往会导致不可行的解决方案。这主要是由于缺乏了解动态交通流。采取务实的做法可能更适合获得某种合理的解决方案。常识和日常观察生活的制定提供了以下的交通控制策略：一个更重的汽车流量的办法是赋予权利的方式为跨越时间间隔为一比一的交通流量较轻的做法不再是交集。他们相应的时间间隔,通过路口应该是平等的。因此,要达到理想的平衡,逻辑策略是让汽车的数量在十字路口排队等候大约相同于这四种。交通的设计控制器结合人类专家知识是在模糊控制系统的框架上建立的。2 模糊逻辑控制模糊逻辑最近成为一个最成功的技术发展,精密的控制和信息系统之一。模糊逻辑是一种简单的方法，程序过程和知识代表人类

5、专家在数字计算机逻辑领域上的基础。例如如果下大雨，然后汽车慢下来是通过接近人类思维的方法来进行处理的,这是理想的定性，语言信息和系统建模的体现。在人类环境中，模糊逻辑控制器表现得更平稳、更稳健地,它应该很快占据了设计的智能，且基于知识的系统自动化和人工智能。模糊逻辑是一种技术,它是自然语言翻译的来描述算法的一种设计政策,是一种算法的数学模型。该数学模型，实现了人的逻辑，体现了灵活性、抽象化、人的逻辑思维推理，在工程解决方案得到很好利用。在1965年模糊逻辑推理是基于模糊理论为基础的,该条规定最近变得成为发展先进的控制和信息系统中最成功的技术发展之一,它有着精密的控制和信息系统。该模型主要包括以

6、下三个主要部分；1、模糊化2、推理使用if-then规则3、去模糊2.1 模糊化 Fuzzification means using the Membership Functions 模糊化是指使用语言变量的隶属函数，该of Linguistic Variables to compute each term's语言变量的隶属函数是用来计算每学期的degree of validity at a specific operation point of the有效期在有效程度的具体操作process.过程。介绍了A fuzzification function is introduced f

7、or一个模糊化功能为each input variable to express the associated每个输入变量来表达相关measurement uncertainty.测量的不确定度。该模糊化的功能的主要目的是解释测量The purpose of thefuzzification function is to interpret measurement oinput variables, each expressed by a real number, as输入变量，由每个数字实数表示一个真正的测量，因为它是逼近各自的更为现实的模糊实数。 2.2 使用if-then原则real n

8、umber所有数值都必须转换成语言的价值。生产规则包括一个条件（中频部分）和结论（后面部分）。中频部分可以由连接起来像与语句语言和或语句语言连接在一起超过一个先决条件。每个规则分配在区间0,1代表个人的重要性规则的支持度。一个结论的正确性的计算方法是指对与支持用复合算子的有效性程度整个状况挂钩。有两种方法，推导出相应的控制输出到一个特定的输入，他们是： 1组成为基础推论2以个人为基础的推理原则2.3 去模糊当一个清晰的数字组成，输出结果值是必需的，在一个模糊集的形式得出的结果应该是去模糊。隶属函数用于重新翻译成一个清晰的模糊输出值。这种重新翻译成一个清晰的模糊输出值的过程被称为模糊化。首先一个

9、典型值是计算每个变量在语言任期最后一个最好的折衷办法是通过平衡出结果使用像中心最大的，面积的中心的不同方法测定，平均最大（月比）等 。3 系统的开发方法本文用以下步骤进行方法的发展:步骤1：电路设计操作电路的智能红绿灯控制器的设计。智能交通信号灯控制系统由四个部分的电路操作。它由输入探测器电路、定时器电路、控制电路和显示电路组成。步骤2：控制器和编程为了运行实时控制算法、创造脉冲的脉宽调制信号和PIC16F877A的使用。由PIC16F877A定点和浮点能力所决定。 该单片机技术结合控制器外设实时处理能力,它为交通灯控制系统和控制器外设制造一个合适的解决方案,使绝大多数的智能交通信号灯控制系统

10、可以很好应用。然后利用模糊化的概念、规则库的概念和去模糊的概念进行处理,整个过程中，这个程序是被写入的。步骤3：保持优化这一步，它是我们在模拟和测试原型设计的第一步。我们的技术，本文使用很大程度上取决于应用类型多。离线优化步骤是完全支持的软件开发工具。如MPLAB IDE。步骤4：在线优化项目建成后，模糊逻辑系统可以实现对目标的硬件平台。在图（1）显示系统的示意图。朗读显示对应的拉丁字符的拼音4 结论该系统的实现需要，因为使用的探测器安装成本较高。在复杂的条件下，实施该制度可以成为有用的，可以给予大幅减少平均延迟和停下车的比例比传统的固定时间的控制的功能。该系统的主要优点是它允许我们描述IF

11、then关系所需的行为。这里的关系要得到手动，这在大型数据集上需要作出重大努力，一些试验和错误是必要的，创造一个满意的模糊控制规则的设置的方法是把这种方法的主要限制之一。它是一个神经网络解决方案的承诺，因为它可以培养自己的数据集。图1： 原理图的智能交通灯控制器原文：DESIGN AND CONSTRUCTION OF INTELLIGENT TRAFFIC LIGHT CONTROL SYSTEM USIN G FUZZY LOGICAIP Conference ProceedingsOctober 7, 2008AbstractVehicular travel is increasing

12、throughout the world, particularly in large urban areas. Therefore the need arises for simulation and optimizing traffic control algorithms to better accommodate this increasing demand. This paper presents a microcontroller simulation of intelligent traffic light controller using fuzzy logic that is

13、 used to change the traffic signal cycles adaptively at a two-way intersection. This paper is an attempt to design an intelligent traffic light control systems using microcontrollers such as PIC 16F84A and PIC 16F877A. And then traffic signal can be controlled depending upon the densities of cars be

14、hind green and red lights of the two-way intersecti on by using sensors and detectors circuits.KeywordsFuzzy logic, Intelligent Traffic light Control System, microcontroller,Smart Infrastructures, Transportation Research.1 Introdu ctionTransportation research has the goal to optimize transportation

15、flow of people and goods. As the number of road users constantly increases, and resources provided by current infrastructures are limited, intelligent control of traffic will become a very important issue in the future. However, some limitations to the usage of intelligent traffic control exist. Avo

16、iding traffic jams for example is thought to be beneficial to both environment and economy, but improved traffic flow may also load to an increase in demand.This thesis discusses the traffic control strategy, which dictates the design criteria for the fuzzy logic controller. Briefly addressed are th

17、e elements and characteristics of the key components of the fuzzy controller-the fuzzifier, the fuzzy rule base formulated by human experts, the fuzzy inference engine based on the Mamdani fuzzy model, and the defuzzifier. The focus, however, is placed on the microcontroller simulation of traffic fl

18、ow regulated by a fuzzy controller. The microcontroller simulation results show the superior performance of the fuzzy controller over that of a conventional controller whose signal cycles are preset. The main goals of this paper are improving safety, minimizing travel time, and increasing the capaci

19、ty of infrastructures. Such improvements are beneficial to health, economy, and the environment, and this show in the allocated budget for IntelligentTransportation Systems. In this paper, the optimization of traffic flow is mainly interested in, thus effectively minimizing average traveling (or wai

20、ting) times for cars.Traffic load is highly dependent on parameters such as time, day, season, weather and unpredictable situations such as accidents, special events or construction activities. If these parameters are not taken into account, the traffic control system will create bottlenecks and del

21、ays. A traffic control system that solves these problems by continuously sensing and adjusting the timing of traffic lights according to the actual traffic load is called an intelligent traffic control system. Traffic flow at an intersection is a complex random process. An attempt to formulate an op

22、timization criterion for traffic control often leads to infeasible solution. This is largely due to lack of understanding the dynamics of traffic flow. Taking a pragmatic approach may be more suited to obtaining some reasonable solution. Common sense and daily-life observations provide the basis for

23、 formulating the following traffic control strategy: The car in an approach with heavier traffic flow is given the right of way to cross the intersection for a time interval longer than an approach with lighter traffic flow. When the traffic corresponding time intervals for passing through the inter

24、section should be about equal in two or more approaches, their corresponding time intervals for passing through the intersection should be equal. Consequently, to achieve the desirable balance, the logical strategy is to make the number of cars waiting in queue at the intersection about the same in

25、the four approaches. The design of such a traffic controller incorporated with human expert knowledge is in the framework of fuzzy control systems.2 Fuzzy L ogic ControlFuzzy logic has recently become one of the most successful technologies for developing sophisticated control and information system

26、s. Fuzzy logic is a simple method representing realworld processes and knowledge of human experts on a digital computer. The logic such as If it rains hard THEN car slows down is close to human reasoning and is ideal to deal with qualitative and linguistic information and system modeling. Fuzzy logi

27、c controllers behave more smoothly and robustly in human environment and should soon dominate the design of Intelligent Knowledge-based System for automation and artificial intelligence.Fuzzy Logic is a technology that translates natural languages description of design policies in to an algorithm us

28、ing a mathematical model. This mathematical model implements the flexibility of human logic, abstraction and thinking in analogies in engineering solutions. Fuzzy logic, based on the theory of fuzzy sets by Zadeh 1965, has recently become one of the most successful technologies for developing sophis

29、ticated control and information systems. This model consists of following three major sections;1. Fuzzification2. Inference using If- Then rules3. Defuzzification2.1 FuzzificationFuzzification means using the Membership Functions of Linguistic Variables to compute each terms degree of validity at a

30、specific operation point of the process. A fuzzification function is introduced for each input variable to express the associatedmeasurement uncertainty. The purpose of the fuzzification function is to interpret measurement of input variables, each expressed by a real number, as more realistic fuzzy

31、 approximations of the respective real numbers.2.2 Inference using If- Then rulesAll numerical values have to be converted into linguistic values. Production rules consist of a condition (IF-part) and a conclusion (THENpart). The IF-part can consists of more than one precondition linked together by

32、linguistic conjunctions like AND and OR. Each rule is assigned a Degree of Support in the interval 0,1 representing the individual importance of the rule. The validity of a conclusion is calculated by a linking of the validity of the entire condition with the degree of support by a composition opera

33、tor. There are two approaches to derive the control output corresponding to a specific input. They are;1. Composition based inference2. Individual-rule based inference2.3 DefuzzificationWhen a crisp (numerical) value of an output result is required, the result in the form of a fuzzy set should be de

34、fuzzified. Membership functions are used to retranslate the fuzzy output into a crisp value. This retranslating is known as defuzzification. First a typical value is computed for each term in thelinguistic variable and finally a best compromise is determined by balancing out the results using differ

35、ent methods like center of maximum(CoM), center of area (CoA), mean of maximum (MoM), etc.3 System Development MethodologyThe development methodology used in this paper has the following steps:Step (1): Circuit DesignThe operating circuit of intelligent traffic light controller is designed. Intellig

36、ent traffic light control system is composed of four parts of circuit operation. It consists of input detector circuit, timer circuit, control circuit and display circuit.Step (2): Controller and ProgrammingIn order to run the real-time control algorithm and create pulse with modulation signals, the

37、 PIC16F877A is used. PIC16F877A consists of fixedpoint and floating-point capability. This microcontroller combines the real-time processing capability with controller peripherals to create a suitable solution for a vast majority of intelligent traffic light control system applications. Then using the concept of fuzzification, rule base and defuzzification, the program is written.Step (3): Off line OptimizationIn this step, we simul