智能交通信號燈畢業(yè)設(shè)計外文翻譯

1、<p><b>　　智能交通信號燈</b></p><p>　　摘要：信號控制是一種必要的措施以確保的質(zhì)量和安全,交通循環(huán)?，F(xiàn)在的信號控制的進一步發(fā)展具有極大的潛力來減少運行時間、車輛、事故成本和整車排放。檢測的發(fā)展和計算機技術(shù)改變了交通信號控制從定時開環(huán)規(guī)定自適應(yīng)反饋控制。目前的自適應(yīng)控制方法,像英國、瑞典MOVA SOS)和英國(孤立的信號(area-wide又控制),采用數(shù)學

2、優(yōu)化與仿真技術(shù)來調(diào)整信號波動的時間觀察到的交通流實時的。優(yōu)化是通過改變時間和周期長度的綠色的信號。在area-wide交叉口控制偏移是之間也發(fā)生了變化。已經(jīng)開發(fā)為幾種方法確定最優(yōu)周期長度和最小延遲在十字路口,但基于不確定性和嚴格的交通信號控制的本質(zhì),全局最優(yōu)是不可能找到的。</p><p>　　1．引文：由于越來越多的公眾意識的環(huán)境影響道路交通許多當局現(xiàn)在所追求的政策來:，管理供求?擁擠，影響模式和路徑選擇?;貫

3、徹“三個代表”重要思想,提高公共汽車?有軌電車和其他公共服務(wù)車輛;設(shè)施提供更好的、更安全,騎自行車和行人的道路使用者等脆弱;降低汽車排放?、噪聲和視覺入侵;為所有道路改善安全?用戶群。</p><p>　　在自適應(yīng)交通信號控制的彈性增強的增加的數(shù)量在周期層疊的綠色階段,從而使數(shù)學優(yōu)化非常復(fù)雜和困難。因為這個原因,自適應(yīng)信號控制在大多數(shù)情況下不是建立在精確的優(yōu)化上,而是建立在綠色的擴展原理。在實踐中,遵循的均勻性是

4、最主要的交通信號控制安全的原因。這一規(guī)定的限制的周期時間和相位的安排。因此,在實踐中是交通信號控制的針對性的解決方案和調(diào)整的基礎(chǔ)上由交通規(guī)劃者。現(xiàn)代可編程信號控制器以大量的可調(diào)參數(shù)是非常適合這一過程。對于好的結(jié)果,一個經(jīng)驗豐富的策劃人和微調(diào)領(lǐng)域中是必要的。模糊控制已經(jīng)被證明是成功的,在這些問題中,精確的數(shù)學建模是困難的或不可能的,但一名有經(jīng)驗的人可以控制的工藝操作。因此,交通信號控制是一種適合于任務(wù)特別為模糊控制。事實上,最古老的文化之

5、一的潛力的例子是一個模擬的模糊控制在一個inter-section交通信號控制的兩個單向的街道。即使在這個非常簡單的情況下,模糊控制是至少在作為一個良好的傳統(tǒng)的自適應(yīng)控制。一般而言,模糊控制是發(fā)現(xiàn)在復(fù)雜問題都優(yōu)于用多目標決策。在交通信號控制多種交通流競爭來自同一時間和空間,而且不同的優(yōu)先選擇往往不同交通流或車輛組。此外,優(yōu)化標準,包括幾個同時喜歡平</p><p>　　2．模糊邏輯：介紹了模糊邏輯,并成功地應(yīng)用于

6、大范圍的自動控制任務(wù)。最大的好處模糊邏輯是有機會模型與不確定的模糊決策。此外,模糊邏輯有能力理解語言指令和控制策略的基礎(chǔ)上產(chǎn)生的先驗的溝通。這一點在利用模糊邏輯來控制理論的基礎(chǔ)上,是模仿人類專家控制的知識,而不是為了構(gòu)建過程本身。的確,模糊控制已經(jīng)被證明是成功的,在這些問題中,精確的數(shù)學建模是困難的或不可能的,但一名有經(jīng)驗的操作員可以控制的過程。一般而言,模糊控制是發(fā)現(xiàn)在復(fù)雜問題都優(yōu)于多目標決策。</p><p>

7、;　　目前,有大量的基于模糊推論系統(tǒng)技術(shù)。不過它們當中的主要部分,受含糊不清的根基;即使它們大都是古典數(shù)學方法表現(xiàn)更好,他們還帶有黑色的盒子,如德模糊化,這是很難證明數(shù)學或邏輯的。例如,如果-然后模糊規(guī)則,它們在核心的模糊推理系統(tǒng),經(jīng)常報道的工作方式,是Ponens概括規(guī)則推理機制的經(jīng)典,但隨便起來就不是這樣的,這之間的關(guān)系,這些規(guī)則和多值邏輯是任何已知的復(fù)雜和人工。此外,專家系統(tǒng)的性能應(yīng)相當于人類專家:它應(yīng)該得到同樣的結(jié)果,專家給,但

8、提醒當控制問題是如此模糊,專家是不確定適當?shù)男袨椤，F(xiàn)有的模糊專家系統(tǒng)很少滿足這第二種情況。</p><p>　　然而,很多研究觀察,模糊推理的方法是基于相似。Kosko,舉個例子,寫的模糊隸屬……代表的相似性定義對象特性的imprecisely。以這句話嚴重,我們學習系統(tǒng)的多值等價,即模糊相似度。原來,從Lukasiewicz多值邏輯的定義,我們能構(gòu)建出一個模糊推理方法的表演,依賴于專家知識推理和只在定義的邏輯概

9、念。所以,我們不需要任何人造的解模糊化方法確定(如重心)決定最后輸出的推斷。我們基本的觀察是,任何的模糊集的生成一個模糊相似度,這些相似之處可以結(jié)合到一個模糊關(guān)系,變成了一個模糊相似度,太。我們把這稱為誘導模糊關(guān)系總模糊相似度。如果-然后模糊推論系統(tǒng)實際上是選擇:比較了每一個問題的IF-part規(guī)則庫以一實際輸入值,找到最相似案例和火相應(yīng)的THEN-part;如果它并非是獨一無二的,使用一個標準賦予了一位專家來進行?；诙嘀颠壿婰uka

10、siewicz welldefined,我們展示如何使用該方法可以正式實施。</p><p>　　假設(shè)和原則模糊交通信號控制交通信號控制是用來最大限度地提高效率的現(xiàn)有交通系統(tǒng)。然而,交通系統(tǒng)的效率,甚至可以模糊。通過提供時間分離的權(quán)利的方式接近流動,交通信號產(chǎn)生深刻影響了效率的交通流。它們能操控的優(yōu)勢或者劣勢的車輛和行人的;取決于權(quán)利的分配方式。因此,正確的應(yīng)用、設(shè)計、安裝、操作和保養(yǎng)維護,交通信號的關(guān)鍵,是安全

11、、高效有序的交通十字路口的運動。</p><p>　　在交通信號控制的,我們都能找到某種中不確定性的許多層面。交通信號控制的輸入是不準確的,而且這也意味著我們無法處理的交通方式的確切位置。可能性是復(fù)雜的控制,并處理這些可能性是一個極其復(fù)雜的任務(wù)。安全、最小化最大化,減少延遲環(huán)境方面的一些目標的控制,但這是很難處理大家聚在一起,傳統(tǒng)的交通信號控制。causeconsequence -關(guān)系的解釋也不可能在交通信號控制

12、。這些都是典型特征的模糊控制。</p><p>　　基于模糊邏輯控制器的設(shè)計來捕獲的關(guān)鍵因素,而不需要控制過程中許多詳細的數(shù)學公式。由于這個事實,他們有許多優(yōu)勢,在實時應(yīng)用。有一個簡單的運算的控制器結(jié)構(gòu),因為它們不需要很多的數(shù)值計算。他們的IFTHEN邏輯推理規(guī)則不需要很多的計算時間。同時,控制器能夠進行了大范圍的輸入,因為不同的控制規(guī)則可適用于他們。如果系統(tǒng)相關(guān)知識是為代表的IFTHEN——簡單模糊控制器的規(guī)則

13、,fuzzy-based可以控制系統(tǒng)具有效率及減輕。的主要目標是確保交通信號控制交叉口安全系統(tǒng)通過保持沖突交通流分開。最優(yōu)性能的十字路口相結(jié)合的系統(tǒng)工程,環(huán)境影響時間價值和交通安全。我們的目標是優(yōu)化系統(tǒng),但是我們需要來決定什么屬性和重量將被用來判斷最優(yōu)。</p><p>　　整個的知識的過程中,系統(tǒng)設(shè)計者對交通信號控制在這種情況下,被控制的能量儲存在規(guī)則知識庫。有一個基本的規(guī)則從而影響系統(tǒng)的閉環(huán)的行為,因此它們應(yīng)

14、該是獲得了徹底。規(guī)則的發(fā)展是很耗時,設(shè)計師經(jīng)常需要為他翻譯過程知識轉(zhuǎn)化為合適的規(guī)則。Sugeno提到的四種方法,推導出惡化模糊控制規(guī)則:</p><p><b>　　1.運營商經(jīng)驗</b></p><p>　　2:控制工程師的知識2,3,6,7,11,14]</p><p>　　3;該主算子的來講模糊建模的控制措施</p><

15、;p><b>　　4.模糊建模的過程</b></p><p><b>　　5.酥脆的建模過程</b></p><p>　　6;髓啟發(fā)式的設(shè)計規(guī)則</p><p>　　7;往往在線改編的規(guī)則。</p><p>　　通常一個組合這些現(xiàn)象的一些方法是必要的,以獲得較好效果。在常規(guī)控制經(jīng)驗,增加設(shè)計的

16、模糊控制器,導致減少開發(fā)時間。</p><p>　　項目的主要目標是FUSICO-research理論分析的模糊交通信號控制,廣義模糊規(guī)則的交通信號控制使用語言變量,驗證了模糊控制原理和校準的隸屬度函數(shù),并發(fā)展了一種模糊自適應(yīng)信號控制器。vehicle-actuated控制的策略,如SOS,MOVA和LHOVRA是控制算法,對第一代。模糊控制算法,該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

17、處理多目標的、多維的和復(fù)雜的交通狀況,如交通信號。模糊控制的典型優(yōu)點是簡單的流程,有效控制,提高產(chǎn)品質(zhì)量。</p><p>　　3． FUSICO：FUSICO-project塑造出的經(jīng)驗的警察。這個規(guī)則庫的發(fā)展是在1996年秋季。j . they Kari正常,經(jīng)驗豐富的交通信號規(guī)劃師,工作時在赫爾辛基理工大學在這個時間。每天工作小組討論,他的經(jīng)驗幫助我們模型對我們的規(guī)則。</p><p&g

18、t;　　在特定情況下病理交通擁堵或很少有車輛在循環(huán);在那里first-in-first-out是唯一合理的控制策略。該算法尋找最相似的實際IF-part輸入值,并給出了相應(yīng)的THEN-part然后被解雇了。交通信號控制系統(tǒng)三個現(xiàn)實的方法來構(gòu)造算法和仿真模型檢驗他們的表現(xiàn)。要解決問題,類似的仿真Mamdani non-fuzzy和古典風格的模糊推理系統(tǒng),也是。結(jié)果對車輛和行人延誤或平均平均車輛延誤,在大多數(shù)情況下更好的在模糊相似度為基礎(chǔ)的

19、控制比在其他的控制系統(tǒng)。比較模糊相似度為基礎(chǔ)的模糊控制的控制和Mamdani風格也強度的假定,在近似推理過程中時,一個基本概念是多值之間的相似的對象,而不是一種概括規(guī)則的推理方式,Ponens經(jīng)典。</p><p>　　FUSICO項目結(jié)果</p><p>　　這個計畫的結(jié)果表明,模糊信號控制的潛力是孤立交叉口控制的一種方法。比較結(jié)果的Pappis-Mamdani控制、模糊孤立的人行過街和

20、模糊兩階段的控制是很不錯的。結(jié)果表明,孤立的人行過街的模糊控制提供了有效的兩種對立的目標妥協(xié),最低行人延誤和最小的車輛的延誤。結(jié)果對兩相控制和Pappis-Mamdani控制表明,模糊控制應(yīng)用領(lǐng)域很廣。改進的最大延時超過20%,這意味著模糊控制的效率可以比傳統(tǒng)的vehicle-actuated控制的效率。</p><p>　　根據(jù)這些結(jié)果,我們可以說,模糊信號控制可以多目標和更有效率,比常規(guī)自適應(yīng)信號控制現(xiàn)在。最

21、大的好處,或許,達到更復(fù)雜的十字路口和環(huán)境。這FUSICO-project仍在繼續(xù)。目的是將一步步的更復(fù)雜的交通信號,并繼續(xù)對模糊控制理論著作。第一個例子將公共交通優(yōu)先考慮的問題。 </p><p>　　原文： Intelligent traffic lights</p><p>　　Abstract：Signal control is a necessary me

22、asure 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 developmen

23、t 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

24、signals) and Britis</p><p>　　1.Citation：As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuing policies to:</p><p>　　? manage deman

25、d and congestion;</p><p>　　? influence mode and route choice;</p><p>　　? improve priority for buses, trams and other public service vehicles;</p><p>　　? provide better and safer fac

26、ilities for pedestrians, cyclists and other vulnerable road users;</p><p>　　? reduce vehicle emissions, noise and visual intrusion; and</p><p>　　? improve safety for all road user groups.</p&

27、gt;<p>　　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

28、that reason, the 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 rea

29、sons. This sets limitations to the cycle time and phase arrangements. Hence,</p><p>　　Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The main benefit of

30、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

31、 communication. The point in utilizing fuzzy logic in control theory is to model control based on human expert knowledge, rather than to model the process itse</p><p>　　At present, there is a multitude of in

32、ference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still contain black boxes, e.g. de fuzzi

33、fication, 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

34、 Ponens rule of infere</p><p>　　Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes 'Fuzzy membership...represents similarities of objects to impreci

35、sely 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

36、able to construct a method performing fuzzy reasoning such that the inference relies only on experts knowledge an</p><p>　　Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is

37、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 ex

38、ert a profound influence on the efficiency of traffic flow. They can operate to the advantage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Conseq</p><p>　　I

39、n 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 po

40、ssibilities are complicated, and handling these possibilities are an extremely complex task. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is diff

41、icult to handle them together in the traditional traffic si</p><p>　　Fuzzy logic based controllers are designed to capture the key factors for controlling a process without requiring many detailed mathematic

42、al formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple computational structure, since they do not require many numerical calculations. The IFTHEN logic of their

43、inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets o</p><p>　　The entire knowledge of the system designer about the p

44、rocess, 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 th

45、oroughly. The development of rules is time consuming, and designers often have to translate process knowledge into appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:</p><p

46、>　　1. operators experience</p><p>　　2. control engineer's knowledge</p><p>　　3. fuzzy modelling of the operator's control actions</p><p>　　4. fuzzy modelling of the proce

47、ss</p><p>　　5. crisp modeling of the process</p><p>　　6. heuristic design rules</p><p>　　7. on-line adaptation of the rules.</p><p>　　Usually a combination of some of t

48、hese methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.</p><p><b>　　3. FUSICO</b&

49、gt;</p><p>　　The main goals of FUSICO-research project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of f

50、uzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOVA and LHOVRA, are the control algorithms of th

51、e first generation. The fuzzy control algorithm can be one of the algorithms of the se</p><p>　　FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. M

52、r. Kari J. Sane, experienced traffic signal planner, was working at the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.</p>&

53、lt;p>　　In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most si

54、milar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their perfor

55、mance. Similar simulations were made to a non-fuzzy and classical Mamd</p><p>　　The results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections

56、. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effect

57、ive compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamda</p><p>　　According to these results, we can say that

58、the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays. The biggest benefits can, probably, be achieved in more complicated intersections and environments. Th

59、e FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities.</p>