交通燈外文翻譯

1、精選優(yōu)質(zhì)文檔-傾情為你奉上精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)專心-專注-專業(yè)精選優(yōu)質(zhì)文檔-傾情為你奉上專心-專注-專業(yè)Traffic lightsSignal 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 vehi

2、cle emissions. The development 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

3、 mathematical optimization 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. S

4、everal methods have been 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.As a result of growing public awareness of the environmental impac

5、t of road traffic 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 v

6、ehicle emissions, 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 r

7、eason, 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 reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic s

8、ignal control 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

9、 needed. Fuzzy 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 th

10、e potentials 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 mul

11、tiobjective 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

12、 and pedestrian 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 t

13、o a wide range 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. T

14、he point in 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 c

15、an control 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 th

16、at classical 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

17、Ponens rule 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

18、, but warn 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.r

19、epresents similarities 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 fuz

20、zy reasoning 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

21、fuzzy similarity, 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

22、actual input 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 Traff

23、ic Signal Control 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 effici

24、ency 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. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and effici

25、ent movement 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 handl

26、ing 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 difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also no

27、t possible 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

28、 applications. 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 contr

29、ol rules can 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

30、 flows apart. 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 desi

31、gner about 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 design

32、ers often have 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

33、 process6. 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.The main goals of FUSICO-r

34、esearch 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-

35、actuated 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, mul

36、ti-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,

37、 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.In particular pathological traffic jams or situations where there are very few vehicles in circulation; there

38、 first-in-first-out is the only reasonable control strategy. The Algorithm is looking for the most similar 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 mode

39、l tested their performance. Similar simulations were made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other c

40、ontrol systems. Comparisons between fuzzy similarity based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.The

41、results of this project have indicated that fuzzy signal control is the potential control method for isolated intersections. 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

42、 that the fuzzy control provides the effective compromise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improveme

43、nt was more than 20 %, which means that the efficiency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control no

44、wadays. The biggest benefits can, probably, be achieved in more complicated intersections and environments. The 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 tr

45、ansport priorities. 交 通 燈信號(hào)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(hào)控制的進(jìn)一步發(fā)展具有極大的潛力來(lái)減少運(yùn)行時(shí)間、車輛、事故成本和整車排放。檢測(cè)的發(fā)展和計(jì)算機(jī)技術(shù)改變了交通信號(hào)控制從定時(shí)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(guó)、瑞典MOVA SOS)和英國(guó)(孤立的信號(hào)(area-wide又控制),采用數(shù)學(xué)優(yōu)化與仿真技術(shù)來(lái)調(diào)整信號(hào)波動(dòng)的時(shí)間觀察到的交通流實(shí)時(shí)的。優(yōu)化是通過(guò)改變時(shí)間和周期長(zhǎng)度的綠色的信號(hào)。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(zhǎng)度和最小延遲在十字路口,但基于不確定性和嚴(yán)格的交通信

46、號(hào)控制的本質(zhì),全局最優(yōu)是不可能找到的。 由于越來(lái)越多的公眾意識(shí)的環(huán)境影響道路交通許多當(dāng)局現(xiàn)在所追求的政策來(lái):，管理供求擁擠，影響模式和路徑選擇;貫徹“三個(gè)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(jué)入侵;為所有道路改善安全用戶群。在自適應(yīng)交通信號(hào)控制的彈性增強(qiáng)的增加的數(shù)量在周期層疊的綠色階段,從而使數(shù)學(xué)優(yōu)化非常復(fù)雜和困難。因?yàn)檫@個(gè)原因,自適應(yīng)信號(hào)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(kuò)展原理。在實(shí)踐中,遵循的均勻性是最主要的交通信號(hào)控制安全的原因。這一規(guī)定的限制的周期時(shí)間和相位的安

47、排。因此,在實(shí)踐中是交通信號(hào)控制的針對(duì)性的解決方案和調(diào)整的基礎(chǔ)上由交通規(guī)劃者?，F(xiàn)代可編程信號(hào)控制器以大量的可調(diào)參數(shù)是非常適合這一過(guò)程。對(duì)于好的結(jié)果,一個(gè)經(jīng)驗(yàn)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(wèn)題中,精確的數(shù)學(xué)建模是困難的或不可能的,但一名有經(jīng)驗(yàn)的人可以控制的工藝操作。因此,交通信號(hào)控制是一種適合于任務(wù)特別為模糊控制。事實(shí)上,最古老的文化之一的潛力的例子是一個(gè)模擬的模糊控制在一個(gè)inter-section交通信號(hào)控制的兩個(gè)單向的街道。即使在這個(gè)非常簡(jiǎn)單的情況下,模糊控制是至少在作為一個(gè)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(wèn)題都優(yōu)于用多目標(biāo)決策。

48、在交通信號(hào)控制多種交通流競(jìng)爭(zhēng)來(lái)自同一時(shí)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(biāo)準(zhǔn),包括幾個(gè)同時(shí)喜歡平均和最大車輛和行人延誤、最大隊(duì)列長(zhǎng)度和百分比停止的車輛。所以,它很可能是很有競(jìng)爭(zhēng)力的模糊控制在復(fù)雜真實(shí)的十字路口的地方傳統(tǒng)的優(yōu)化方法的使用是有問(wèn)題的。介紹了模糊邏輯,并成功地應(yīng)用于大范圍的自動(dòng)控制任務(wù)。最大的好處模糊邏輯是有機(jī)會(huì)模型與不確定的模糊決策。此外,模糊邏輯有能力理解語(yǔ)言指令和控制策略的基礎(chǔ)上產(chǎn)生的先驗(yàn)的溝通。這一點(diǎn)在利用模糊邏輯來(lái)控制理論的基礎(chǔ)上,是模仿人類專家控制的知識(shí),而不是為了構(gòu)建過(guò)程本身。的確,模糊控制已經(jīng)被證明是成功的,在這些問(wèn)題中,精確的數(shù)學(xué)建模是困

49、難的或不可能的,但一名有經(jīng)驗(yàn)的操作員可以控制的過(guò)程。一般而言,模糊控制是發(fā)現(xiàn)在復(fù)雜問(wèn)題都優(yōu)于多目標(biāo)決策。目前,有大量的基于模糊推論系統(tǒng)技術(shù)。不過(guò)它們當(dāng)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(xué)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(xué)或邏輯的。例如,如果-然后模糊規(guī)則,它們?cè)诤诵牡哪：评硐到y(tǒng),經(jīng)常報(bào)道的工作方式,是Ponens概括規(guī)則推理機(jī)制的經(jīng)典,但隨便起來(lái)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(dāng)于人類專家:它應(yīng)該得到同樣的結(jié)果,專家給,但提醒當(dāng)控制問(wèn)題是如此模糊,專家是不確定適當(dāng)?shù)男袨椤，F(xiàn)有的模糊專

50、家系統(tǒng)很少滿足這第二種情況。然而,很多研究觀察,模糊推理的方法是基于相似。Kosko,舉個(gè)例子,寫的模糊隸屬代表的相似性定義對(duì)象特性的imprecisely。以這句話嚴(yán)重,我們學(xué)習(xí)系統(tǒng)的多值等價(jià),即模糊相似度。原來(lái),從Lukasiewicz多值邏輯的定義,我們能構(gòu)建出一個(gè)模糊推理方法的表演,依賴于專家知識(shí)推理和只在定義的邏輯概念。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(gè)模糊相似度,這些相似之處可以結(jié)合到一個(gè)模糊關(guān)系,變成了一個(gè)模糊相似度,太。我們把這稱為誘導(dǎo)模糊關(guān)系總模糊相似度。如果-然后模糊推論系統(tǒng)實(shí)際上是選擇:比較了每

51、一個(gè)問(wèn)題的IF-part規(guī)則庫(kù)以一實(shí)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(dú)一無(wú)二的,使用一個(gè)標(biāo)準(zhǔn)賦予了一位專家來(lái)進(jìn)行?；诙嘀颠壿婰ukasiewicz welldefined,我們展示如何使用該方法可以正式實(shí)施。假設(shè)和原則模糊交通信號(hào)控制交通信號(hào)控制是用來(lái)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(guò)提供時(shí)間分離的權(quán)利的方式接近流動(dòng),交通信號(hào)產(chǎn)生深刻影響了效率的交通流。它們能操控的優(yōu)勢(shì)或者劣勢(shì)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(jì)、安裝、操作和保養(yǎng)維護(hù),交通信號(hào)的關(guān)鍵,是安全、高效有序的交通十字路口的運(yùn)動(dòng)。在交通

52、信號(hào)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(hào)控制的輸入是不準(zhǔn)確的,而且這也意味著我們無(wú)法處理的交通方式的確切位置?？赡苄允菑?fù)雜的控制,并處理這些可能性是一個(gè)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(biāo)的控制,但這是很難處理大家聚在一起,傳統(tǒng)的交通信號(hào)控制。causeconsequence -關(guān)系的解釋也不可能在交通信號(hào)控制。這些都是典型特征的模糊控制?；谀：壿嬁刂破鞯脑O(shè)計(jì)來(lái)捕獲的關(guān)鍵因素,而不需要控制過(guò)程中許多詳細(xì)的數(shù)學(xué)公式。由于這個(gè)事實(shí),他們有許多優(yōu)勢(shì),在實(shí)時(shí)應(yīng)用。有一個(gè)簡(jiǎn)單的運(yùn)算的控制器結(jié)構(gòu),因?yàn)樗鼈儾恍枰芏嗟臄?shù)值計(jì)算。他們的IFTHEN邏輯推理規(guī)則不

53、需要很多的計(jì)算時(shí)間。同時(shí),控制器能夠進(jìn)行了大范圍的輸入,因?yàn)椴煌目刂埔?guī)則可適用于他們。如果系統(tǒng)相關(guān)知識(shí)是為代表的IFTHEN簡(jiǎn)單模糊控制器的規(guī)則,fuzzy-based可以控制系統(tǒng)具有效率及減輕。的主要目標(biāo)是確保交通信號(hào)控制交叉口安全系統(tǒng)通過(guò)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(shí)間價(jià)值和交通安全。我們的目標(biāo)是優(yōu)化系統(tǒng),但是我們需要來(lái)決定什么屬性和重量將被用來(lái)判斷最優(yōu)。整個(gè)的知識(shí)的過(guò)程中,系統(tǒng)設(shè)計(jì)者對(duì)交通信號(hào)控制在這種情況下,被控制的能量?jī)?chǔ)存在規(guī)則知識(shí)庫(kù)。有一個(gè)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)該是獲得了徹底。規(guī)則的發(fā)展是很耗時(shí),設(shè)計(jì)師經(jīng)常需要為他翻譯過(guò)

54、程知識(shí)轉(zhuǎn)化為合適的規(guī)則。Sugeno提到的四種方法,推導(dǎo)出惡化模糊控制規(guī)則:1.運(yùn)營(yíng)商經(jīng)驗(yàn)2:控制工程師的知識(shí)2,3,6,7,11,143;該主算子的來(lái)講模糊建模的控制措施4.模糊建模的過(guò)程5.酥脆的建模過(guò)程6;髓啟發(fā)式的設(shè)計(jì)規(guī)則7;往往在線改編的規(guī)則。通常一個(gè)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(yàn),增加設(shè)計(jì)的模糊控制器,導(dǎo)致減少開發(fā)時(shí)間。項(xiàng)目的主要目標(biāo)是FUSICO-research理論分析的模糊交通信號(hào)控制,廣義模糊規(guī)則的交通信號(hào)控制使用語(yǔ)言變量,驗(yàn)證了模糊控制原理和校準(zhǔn)的隸屬度函數(shù),并發(fā)展了一種模糊自適應(yīng)信號(hào)控制器。vehicle-actuated控制的策略,如S

55、OS,MOVA和LHOVRA是控制算法,對(duì)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處理多目標(biāo)的、多維的和復(fù)雜的交通狀況,如交通信號(hào)。模糊控制的典型優(yōu)點(diǎn)是簡(jiǎn)單的流程,有效控制,提高產(chǎn)品質(zhì)量。FUSICO-project塑造出的經(jīng)驗(yàn)的警察。這個(gè)規(guī)則庫(kù)的發(fā)展是在1996年秋季。j . they Kari正常,經(jīng)驗(yàn)豐富的交通信號(hào)規(guī)劃師,工作時(shí)在赫爾辛基理工大學(xué)在這個(gè)時(shí)間。每天工作小組討論,他的經(jīng)驗(yàn)幫助我們模型對(duì)我們的規(guī)則。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(shí)際IF

56、-part輸入值,并給出了相應(yīng)的THEN-part然后被解雇了。交通信號(hào)控制系統(tǒng)三個(gè)現(xiàn)實(shí)的方法來(lái)構(gòu)造算法和仿真模型檢驗(yàn)他們的表現(xiàn)。要解決問(wèn)題,類似的仿真Mamdani non-fuzzy和古典風(fēng)格的模糊推理系統(tǒng),也是。結(jié)果對(duì)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(fēng)格也強(qiáng)度的假定,在近似推理過(guò)程中時(shí),一個(gè)基本概念是多值之間的相似的對(duì)象,而不是一種概括規(guī)則的推理方式,Ponens經(jīng)典。FUSICO項(xiàng)目結(jié)果這個(gè)計(jì)畫的結(jié)果表明,模糊信號(hào)控制的潛力是孤立交叉口控制的一種方法。比較結(jié)果的Papp

57、is-Mamdani控制、模糊孤立的人行過(guò)街和模糊兩階段的控制是很不錯(cuò)的。結(jié)果表明,孤立的人行過(guò)街的模糊控制提供了有效的兩種對(duì)立的目標(biāo)妥協(xié),最低行人延誤和最小的車輛的延誤。結(jié)果對(duì)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(jìn)的最大延時(shí)超過(guò)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。根據(jù)這些結(jié)果,我們可以說(shuō),模糊信號(hào)控制可以多目標(biāo)和更有效率,比常規(guī)自適應(yīng)信號(hào)控制現(xiàn)在。最大的好處,或許,達(dá)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(hào),并繼續(xù)對(duì)模糊控制理論著作。第一個(gè)例子將公共

58、交通優(yōu)先考慮的問(wèn)題。- 高氯酸對(duì)阿膠進(jìn)行濕法消化后, 用導(dǎo)數(shù)火焰原子吸收光譜技術(shù)測(cè)定阿膠中的銅、“中藥三大寶, 人參、鹿茸和阿膠。”阿膠的藥用已有兩千多年的悠久歷史, 歷代宮 馬作峰.論疲勞源于肝臟J.廣西中醫(yī)藥,2008,31(1):31. 史麗萍,馬東明, 解麗芳等.力竭性運(yùn)動(dòng)對(duì)小鼠肝臟超微結(jié)構(gòu)及肝糖原、肌糖元含量的影響J. 遼寧中醫(yī)雜志, 王輝武,吳行明,鄧開蓉.內(nèi)經(jīng)“肝者罷極之本”的臨床價(jià)值J . 成都中醫(yī)藥大學(xué)學(xué)報(bào),1997,20(2):9. 楊維益,陳家旭,王天芳等.運(yùn)動(dòng)性疲勞與中醫(yī)肝臟的關(guān)系J.北京中醫(yī)藥大學(xué)學(xué)報(bào). 1996,19(1):8.1 運(yùn)動(dòng)性疲勞與肝臟 張俊明.“高效強(qiáng)

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