具有車流量檢測(cè)功能的智能交通燈設(shè)計(jì) -外文翻譯

1、本科畢業(yè)設(shè)計(jì)(論文)外文翻譯浙江師范大學(xué)本科畢業(yè)設(shè)計(jì)(論文)外文翻譯譯文：設(shè)計(jì)和施工的具有油井G模糊邏輯控制系統(tǒng)的智能紅綠燈AIP會(huì)議論文集2008年10月7日摘要車載旅行的增加遍布世界各地,尤其是在大型的都會(huì)區(qū)域中。因此,生活中往往有很多需要交通燈的地方是需要進(jìn)行模擬和優(yōu)化交通控制算法,因此提出了一種更好的適應(yīng)這種交通控制器方案。本文介紹了一種利用單片機(jī)控制的智能交通的模擬光控制器，該系統(tǒng)采用模糊邏輯是用來改變交通信號(hào)周期自適應(yīng)地在一個(gè)雙向的十字路口進(jìn)行紅綠控制。文章試圖通過設(shè)計(jì)一個(gè)智能的交通燈控制系統(tǒng),如PIC 16F84A微控制器和PIC 16F877A微控制器。然后交通信號(hào)可以控制的密

2、度取決于背后的雙向綠色和紅色的燈光。關(guān)鍵詞模糊邏輯；智能交通信號(hào)燈控制系統(tǒng)；單片機(jī)；智能的基礎(chǔ)設(shè)施；交通研究1 簡介運(yùn)輸?shù)难芯磕繕?biāo)是以優(yōu)化交通流的人員和貨物為基礎(chǔ)。隨著交流量數(shù)量的不斷增加,道路使用者由現(xiàn)行的基礎(chǔ)設(shè)施和提供的資源是有限的,未來智能控制的交通將成為一個(gè)非常重要的問題。然而,一些限制的使用智能交通控制的問題依然存在。例如避免交通擁堵被認(rèn)為是對(duì)雙方都有利的環(huán)境和經(jīng)濟(jì)，但也可能改善交通流負(fù)載需求增加。本文討論了交通管制策略,這就決定了模糊邏輯控制器的設(shè)計(jì)標(biāo)準(zhǔn)。簡要討論的要素及模糊控制器的模糊化，模糊規(guī)則庫由人類專家，模糊推理機(jī)的模糊模型為基礎(chǔ)制定，并利用去模糊的特點(diǎn)，使擺在交通流微控制

3、器上由模糊控制器調(diào)節(jié)仿真。微控制器仿真結(jié)果表明該模糊性能優(yōu)越控制器是一個(gè)傳統(tǒng)的控制器，其信號(hào)周期可預(yù)置。 本文的主要目標(biāo)是提高其安全性,減少出行時(shí)間、增加基礎(chǔ)設(shè)施的能力。這種改進(jìn)的交通控制系統(tǒng)有益健康、經(jīng)濟(jì)、環(huán)境,這個(gè)智能交通系統(tǒng)可分配預(yù)算。本文的優(yōu)化、交通流的變化是非常重要的,從而有效減少平均旅行(或者等待)的時(shí)間。交通負(fù)荷是個(gè)高度依賴參數(shù)的系統(tǒng)，它有很多參數(shù)，如時(shí)間、日、季節(jié)、天氣和一些不可預(yù)知的情況,比如事故,特殊事件或施工活動(dòng)。如果這些參數(shù)不能考慮交通控制系統(tǒng),將創(chuàng)造瓶頸和延遲。交通管制系統(tǒng)，遙感和解決不斷調(diào)整交通根據(jù)實(shí)際交通負(fù)荷燈時(shí)間被稱為智能交通控制系統(tǒng)中的瓶頸和延遲。交通流在一個(gè)

4、交叉口是一個(gè)復(fù)雜的隨機(jī)過程。試圖制定出一個(gè)優(yōu)化判斷的交通管制往往會(huì)導(dǎo)致不可行的解決方案。這主要是由于缺乏了解動(dòng)態(tài)交通流。采取務(wù)實(shí)的做法可能更適合獲得某種合理的解決方案。常識(shí)和日常觀察生活的制定提供了以下的交通控制策略：一個(gè)更重的汽車流量的辦法是賦予權(quán)利的方式為跨越時(shí)間間隔為一比一的交通流量較輕的做法不再是交集。他們相應(yīng)的時(shí)間間隔,通過路口應(yīng)該是平等的。因此,要達(dá)到理想的平衡,邏輯策略是讓汽車的數(shù)量在十字路口排隊(duì)等候大約相同于這四種。交通的設(shè)計(jì)控制器結(jié)合人類專家知識(shí)是在模糊控制系統(tǒng)的框架上建立的。2 模糊邏輯控制模糊邏輯最近成為一個(gè)最成功的技術(shù)發(fā)展,精密的控制和信息系統(tǒng)之一。模糊邏輯是一種簡單的

5、方法，程序過程和知識(shí)代表人類專家在數(shù)字計(jì)算機(jī)邏輯領(lǐng)域上的基礎(chǔ)。例如如果下大雨，然后汽車慢下來是通過接近人類思維的方法來進(jìn)行處理的,這是理想的定性，語言信息和系統(tǒng)建模的體現(xiàn)。在人類環(huán)境中，模糊邏輯控制器表現(xiàn)得更平穩(wěn)、更穩(wěn)健地,它應(yīng)該很快占據(jù)了設(shè)計(jì)的智能，且基于知識(shí)的系統(tǒng)自動(dòng)化和人工智能。模糊邏輯是一種技術(shù),它是自然語言翻譯的來描述算法的一種設(shè)計(jì)政策,是一種算法的數(shù)學(xué)模型。該數(shù)學(xué)模型，實(shí)現(xiàn)了人的邏輯，體現(xiàn)了靈活性、抽象化、人的邏輯思維推理，在工程解決方案得到很好利用。在1965年模糊邏輯推理是基于模糊理論為基礎(chǔ)的,該條規(guī)定最近變得成為發(fā)展先進(jìn)的控制和信息系統(tǒng)中最成功的技術(shù)發(fā)展之一,它有著精密的控制

6、和信息系統(tǒng)。該模型主要包括以下三個(gè)主要部分；1、模糊化2、推理使用if-then規(guī)則3、去模糊2.1 模糊化 Fuzzification means using the Membership Functions 模糊化是指使用語言變量的隸屬函數(shù)，該of Linguistic Variables to compute each term's語言變量的隸屬函數(shù)是用來計(jì)算每學(xué)期的degree of validity at a specific operation point of the有效期在有效程度的具體操作process.過程。介紹了A fuzzification function i

7、s introduced for一個(gè)模糊化功能為each input variable to express the associated每個(gè)輸入變量來表達(dá)相關(guān)measurement uncertainty.測(cè)量的不確定度。該模糊化的功能的主要目的是解釋測(cè)量The purpose of thefuzzification function is to interpret measurement oinput variables, each expressed by a real number, as輸入變量，由每個(gè)數(shù)字實(shí)數(shù)表示一個(gè)真正的測(cè)量，因?yàn)樗潜平髯缘母鼮楝F(xiàn)實(shí)的模糊實(shí)數(shù)。 2.2 使用i

8、f-then原則real number所有數(shù)值都必須轉(zhuǎn)換成語言的價(jià)值。生產(chǎn)規(guī)則包括一個(gè)條件（中頻部分）和結(jié)論（后面部分）。中頻部分可以由連接起來像與語句語言和或語句語言連接在一起超過一個(gè)先決條件。每個(gè)規(guī)則分配在區(qū)間0,1代表個(gè)人的重要性規(guī)則的支持度。一個(gè)結(jié)論的正確性的計(jì)算方法是指對(duì)與支持用復(fù)合算子的有效性程度整個(gè)狀況掛鉤。有兩種方法，推導(dǎo)出相應(yīng)的控制輸出到一個(gè)特定的輸入，他們是： 1組成為基礎(chǔ)推論2以個(gè)人為基礎(chǔ)的推理原則2.3 去模糊當(dāng)一個(gè)清晰的數(shù)字組成，輸出結(jié)果值是必需的，在一個(gè)模糊集的形式得出的結(jié)果應(yīng)該是去模糊。隸屬函數(shù)用于重新翻譯成一個(gè)清晰的模糊輸出值。這種重新翻譯成一個(gè)清晰的模糊輸出值

9、的過程被稱為模糊化。首先一個(gè)典型值是計(jì)算每個(gè)變量在語言任期最后一個(gè)最好的折衷辦法是通過平衡出結(jié)果使用像中心最大的，面積的中心的不同方法測(cè)定，平均最大（月比）等 。3 系統(tǒng)的開發(fā)方法本文用以下步驟進(jìn)行方法的發(fā)展:步驟1：電路設(shè)計(jì)操作電路的智能紅綠燈控制器的設(shè)計(jì)。智能交通信號(hào)燈控制系統(tǒng)由四個(gè)部分的電路操作。它由輸入探測(cè)器電路、定時(shí)器電路、控制電路和顯示電路組成。步驟2：控制器和編程為了運(yùn)行實(shí)時(shí)控制算法、創(chuàng)造脈沖的脈寬調(diào)制信號(hào)和PIC16F877A的使用。由PIC16F877A定點(diǎn)和浮點(diǎn)能力所決定。 該單片機(jī)技術(shù)結(jié)合控制器外設(shè)實(shí)時(shí)處理能力,它為交通燈控制系統(tǒng)和控制器外設(shè)制造一個(gè)合適的解決方案,使絕大

10、多數(shù)的智能交通信號(hào)燈控制系統(tǒng)可以很好應(yīng)用。然后利用模糊化的概念、規(guī)則庫的概念和去模糊的概念進(jìn)行處理,整個(gè)過程中，這個(gè)程序是被寫入的。步驟3：保持優(yōu)化這一步，它是我們?cè)谀M和測(cè)試原型設(shè)計(jì)的第一步。我們的技術(shù)，本文使用很大程度上取決于應(yīng)用類型多。離線優(yōu)化步驟是完全支持的軟件開發(fā)工具。如MPLAB IDE。步驟4：在線優(yōu)化項(xiàng)目建成后，模糊邏輯系統(tǒng)可以實(shí)現(xiàn)對(duì)目標(biāo)的硬件平臺(tái)。在圖（1）顯示系統(tǒng)的示意圖。朗讀顯示對(duì)應(yīng)的拉丁字符的拼音4 結(jié)論該系統(tǒng)的實(shí)現(xiàn)需要，因?yàn)槭褂玫奶綔y(cè)器安裝成本較高。在復(fù)雜的條件下，實(shí)施該制度可以成為有用的，可以給予大幅減少平均延遲和停下車的比例比傳統(tǒng)的固定時(shí)間的控制的功能。該系統(tǒng)的主

11、要優(yōu)點(diǎn)是它允許我們描述IF then關(guān)系所需的行為。這里的關(guān)系要得到手動(dòng)，這在大型數(shù)據(jù)集上需要作出重大努力，一些試驗(yàn)和錯(cuò)誤是必要的，創(chuàng)造一個(gè)滿意的模糊控制規(guī)則的設(shè)置的方法是把這種方法的主要限制之一。它是一個(gè)神經(jīng)網(wǎng)絡(luò)解決方案的承諾，因?yàn)樗梢耘囵B(yǎng)自己的數(shù)據(jù)集。圖1： 原理圖的智能交通燈控制器原文：DESIGN AND CONSTRUCTION OF INTELLIGENT TRAFFIC LIGHT CONTROL SYSTEM USIN G FUZZY LOGICAIP Conference ProceedingsOctober 7, 2008AbstractVehicular travel

12、is increasing 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

13、 logic that is 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 densit

14、ies of cars behind 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 t

15、ransportation 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 cont

16、rol exist. Avoiding 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 ad

17、dressed are the 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

18、 of traffic flow 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 increas

19、ing the capacity 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 tra

20、veling (or waiting) 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 bottl

21、enecks and delays. 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 f

22、ormulate an optimization 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

23、 the basis for 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 thr

24、ough the intersection 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 abou

25、t the same in 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 info

26、rmation systems. 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 modeli

27、ng. Fuzzy logic 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 a

28、n algorithm using 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 dev

29、eloping sophisticated 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

30、validity at a 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 r

31、ealistic fuzzy 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 linke

32、d together by 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 com

33、position operator. 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 se

34、t should be defuzzified. 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 result

35、s using different 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 desi

36、gned. Intelligent 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 modulatio

37、n signals, the 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