智能科學(xué)與技術(shù)外文翻譯

1、<p><b>　　西安郵電大學(xué)</b></p><p>　　畢 業(yè) 設(shè) 計（論 文）</p><p><b>　　外文文獻翻譯</b></p><p>　　院（系）： 自動化學(xué)院 </p><p>　　專 業(yè)： 智能科學(xué)與技術(shù)

2、 </p><p>　　班 級： 智能0801班 </p><p>　　學(xué)生姓名： 萬云艷 </p><p>　　導(dǎo)師姓名： 韓中 職稱： 講師 </p><p>　　起止時間：2012年 3月8日 至 20

3、12年 6月20日</p><p><b>　　英文原文</b></p><p>　　298 SHEARD AND MOSTASHARI</p><p>　　Table I. Complex System Examples</p><p>　　Systems Engineering DOI 10.1002/sy

4、s</p><p>　　PRINCIPLES OF COMPLEX SYSTEMS FOR SYSTEMS ENGINEERING 299</p><p>　　reason all should be amenable to improvement based on the principles for engineering complex systems addressed

5、in later sections. Systems engineers should be familiar with the three examples:</p><p>　　?·INCOSE </p><p>　　·The systems engineering (SE) process within a company </p><p&g

6、t;　　·The National Airspace System (air traffic control system). </p><p>　　Table I shows that all three examples have all complex systems attributes listed above and therefore that these are complex syst

7、ems.</p><p>　　3.2. Systems-of-Systems</p><p>　　It should be noted that Systems-of-Systems (SoSs) is currently of great interest to systems engineers. The topic was originally defined by [Maier,

8、1998]; confer-ences and papers addressing systems-of-systems have increased greatly in the last few years. Systems-of-sys-tems issues that differ from systems issues include:</p><p>　　Integration of independ

9、ently-operational compo-nent systems that were built for other purposes </p><p>　　?Rapid evolution of both user needs and system technologies, which prevents stable requirements </p><p>　　?Mul

10、tiple disparate stakeholders with conflicting needs and a lack of incentives to participate in the system-of-systems </p><p>　　?Distributed development and its consequent com-munication problems </p>

11、<p>　　Dependence on an integrated computing infra-structure that has extremely high and increasing complexity, thus threatening unintended conse-quences. </p><p>　　In an engineering context, systems-of

12、-systems are often, but not always, complex systems. Figure 2 shows this comparison. Systems-of-systems usually come up in a program acquisition context, and are distinguished as being unmanageable using standard top-tow

13、n sys-tems engineering, whereas complex systems usually come up in an analytical or scientific context, and are described as being not decomposable.</p><p>　　Most systems-of-systems are also complex systems

14、(CxSs) and vice versa; hence the two top circles overlap greatly. A system such as Joint Strike Fighter that is developed via a program manager and a chief engineer is by definition not a complex system (there are not in

15、dependent agents). However, it specifically is consid-ered a system-of-systems in some Defense Department work [Chairman, Joint Chiefs of Staff, 2007], although not according to the more generally accepted definition [DO

16、D AT&L, 200</p><p>　　4. COMPLEX SYSTEMS SCIENCE INSIGHTS</p><p>　　Complex systems science includes a number of current research areas all having to do in some ways with complexity, complex s

17、ystems, or nonlinear systems. Some examples include complexity theory, chaos the-ory, cellular automata, and nonlinear dynamics. Taken as a whole, these sciences offer the following insights, which have important systems

18、 engineering potential [Sheard, 2006]:</p><p>　　Emergence: Emergence is related to the depend-ence of the whole on parts, the interdependence of parts, and specialization of parts. While study-ing the parts

19、in isolation does not work, the nature of complex systems can be probed by investigat-ing how changes in one part affect the others, and the behavior of the whole. </p><p>　　Pattern formation: Simple mathema

20、tical mod-els capture pattern formation such as local activa-tion / long range inhibition. </p><p>　　Multiple (meta-) stable states: Small displace-ments (perturbations) lead to recovery, and larger ones can

21、 lead to radical changes of properties. Dynamics do not average simply. </p><p>　　Multiscale descriptions are needed to under-stand complex systems. Fine scales influence large scale behavior. </p>&l

22、t;p>　　It is difficult but not impossible to answer the question “How complex is it?” </p><p>　　Behavior (response) complexity: To describe the behavior of a system we try to describe the re-sponse functio

23、n: actions as a function of the en-vironment. However, unless simplifying assumptions are made, this requires an amount of information that grows exponentially with the complexity of the environment. </p><p>

24、;　　Contrasts: Complex systems often exhibit con-trasting characteristics, including simplicity and complexity, order and disorder, random and pre-dictable behavior, repeating patterns and change.</p><p>　　We

25、 cannot predict what a complex system will evolve into. </p><p>　　The complex adaptive systems cycle, created by Gell-Mann [1994a], is shown in the first column of Table II. The other three columns show how

26、our exam-ple systems evolve in accordance with this cycle.</p><p>　　The first step in the cycle is abstracting from the real world to a model. This involves a tradeoff between fine-graining and coarseness. T

27、he second step is iden-tifying regularities or patterns in the abstracted infor-mation. It is often difficult to sort out what is random from what is informative or patterns. The third step is organizing these regulariti

28、es into a schema. This essentially compresses the information into something sim-pler; how much compression is acceptable is a judg-ment call</p><p>　　It is worthwhile to note that in explaining this cycle,

29、Dr. Gell-Mann [1994b] was concentrating on biological systems rather than on systems engineered by man, so the applicability of the cycle to man-made complex systems is suggestive of a general truth.</p><p>

30、　　300 SHEARD AND MOSTASHARI</p><p>　　Figure 2. Systems of systems compared to complex systems. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]</p&g

31、t;<p>　　Table II. Complex Adaptive Systems Cycle Applied to Examples</p><p><b>　　中文譯文</b></p><p>　　298 謝爾德和莫斯塔沙瑞</p><p>　　表格一：復(fù)雜系統(tǒng)的例子</p><p>　　復(fù)雜系

32、統(tǒng)系統(tǒng)工程的原理 299</p><p>　　原因都應(yīng)該服從改善為基礎(chǔ)的原則，在后面的章節(jié)中涉及的復(fù)雜的系統(tǒng)工程。系統(tǒng)工程師應(yīng)該熟悉的三個例子：</p><p><b>　　·國際系統(tǒng)工程協(xié)會</b></p><p>　　·在公司內(nèi)系統(tǒng)工程（SE）的過程</p><p>　　·國家空域系統(tǒng)（

33、空中交通管制系統(tǒng)）。</p><p>　　表一顯示，所有三個例子，所有復(fù)雜的系統(tǒng)屬性中列出以上，因此，這些都是復(fù)雜的系統(tǒng)。</p><p><b>　　3.2.系統(tǒng)的系統(tǒng)</b></p><p>　　應(yīng)當(dāng)指出的系統(tǒng)系統(tǒng)（SOSS）是目前系統(tǒng)工程師的極大興趣。最初定義的主題[1998]邁爾;賦予差異和文件處理系統(tǒng)的系統(tǒng)已經(jīng)在過去幾年中大大增加。&

34、#160;SYS-TEMS系統(tǒng)的問題，從系統(tǒng)的問題不同，包括：</p><p>　　獨立運作的組分NENT被用于其他目的的系統(tǒng)集成</p><p>　　快速演變的用戶需求和系統(tǒng)技術(shù)，從而防止了穩(wěn)定的需求</p><p>　　矛盾需要多個不同的利益相關(guān)者和缺乏激勵機制，參與系統(tǒng)的系統(tǒng)</p><p>　　分布式的發(fā)展和其隨后的通信問題</

35、p><p>　　綜合計算基礎(chǔ)結(jié)構(gòu)，具有極高的和日益復(fù)雜的依賴性，從而威脅的意外conse-序列。 在工程方面，往往是系統(tǒng)的系統(tǒng)，但并不總是，復(fù)雜的系統(tǒng)。圖2顯示了這種比較。系統(tǒng)的系統(tǒng)通常會在收購方案方面，為無力，使用標(biāo)準(zhǔn)的頂級鎮(zhèn)SYS-TEMS工程，而復(fù)雜的系統(tǒng)通常在分析或科學(xué)方面的區(qū)別，不腐化。</p><p>　　大多數(shù)系統(tǒng)的系統(tǒng)也復(fù)雜系統(tǒng)（CxSs），反之亦然，因此前兩個圓圈重疊大大。如

36、聯(lián)合攻擊戰(zhàn)斗機的系統(tǒng)開發(fā)，通過項目經(jīng)理和總工程師，是不是一個復(fù)雜的系統(tǒng)（有沒有獨立的代理人）的定義。然而，它專門consid-ERED等效1系統(tǒng)，系統(tǒng)在一些國防部署工作[主席，參謀長聯(lián)席會議，2007]，雖然未按更普遍接受的定義[國防部的AT大號，2006]，這是更為像梅爾定義。特設(shè)系統(tǒng)，系統(tǒng)在長周期自上而下開發(fā)的系統(tǒng)相比，被拉到一起在最后一分鐘由操作人通道，并沒有首席系統(tǒng)集成商，也不是一個特定的發(fā)展時期[摩天輪，2006年]。這些最沒

37、有資格作為復(fù)雜的系統(tǒng)。復(fù)雜的系統(tǒng)，包括大量的基本粒子，或者是不相關(guān)的，以工程的生物系統(tǒng)將不被視為系統(tǒng)的系統(tǒng)。</p><p>　　4.科學(xué)洞察復(fù)雜系統(tǒng)復(fù)雜系統(tǒng)的性質(zhì)，可以探測由investigat的一部分變化是如何影響他人，而全的行為。復(fù)雜系統(tǒng)科學(xué)包括了目前的研究領(lǐng)域都不必做某些方面的復(fù)雜性，復(fù)雜系統(tǒng)，非線性系統(tǒng)的數(shù)量。一些例子包括復(fù)雜性理論，混亂理論，元胞自動機，非線性動力學(xué)。作為一個整體，這些科學(xué)提供了以下的

38、看法，其中有工程的重要系統(tǒng)的潛力[謝爾德，2006]： 興起的出現(xiàn)，是取決于整個ENCE的部件，零件的相互依存關(guān)系，以及零件的??專業(yè)化。雖然學(xué)習(xí)ING在隔離的部分不起作用。</p><p>　　格局的形成：簡單的數(shù)學(xué)模ELS捕捉格局的形成，如本地激活/抑制遠距離。 多（元）穩(wěn)定狀態(tài)：小位移（擾動）導(dǎo)致的復(fù)蘇，并可能導(dǎo)致更大的徹底改變物業(yè)。動力學(xué)不要簡單平均</p><p>　　多尺度

39、描述下復(fù)雜系統(tǒng)的需要。細密的鱗片影響大規(guī)模的行為。</p><p>　　它是很難的，但不是不可能回答的問題“，它是多么復(fù)雜？”</p><p>　　行為（響應(yīng)）的復(fù)雜性：為了描述一個系統(tǒng)，我們嘗試來形容再響應(yīng)函數(shù)的行為：作為一個功能的連接環(huán)境的行動。然而，除非作出簡化假設(shè)，這需要一個信息量成倍增長與環(huán)境的復(fù)雜性。</p><p>　　對比：復(fù)雜系統(tǒng)往往表現(xiàn)出CON-

40、trasting的特點，包括簡單和復(fù)雜性，秩序和無序，隨機行為，重復(fù)圖案和變化。</p><p>　　我們無法預(yù)測什么將演變成一個復(fù)雜的系統(tǒng)。</p><p>　　復(fù)雜自適應(yīng)系統(tǒng)循環(huán)，由蓋爾曼創(chuàng)造的[1994a]，在第一列的表二所示。其他三個列顯示我們的考試例如系統(tǒng)如何按照這個周期的演變。</p><p>　　在循環(huán)的第一步是從現(xiàn)實世界的抽象模型。這涉及到一個精細的

41、木紋和粗糙之間的權(quán)衡。第二步是在抽象的信息信息的IDEN-tifying規(guī)律或模式。它往往是很難弄清楚什么是隨機的，什么是信息或圖案。第三步是組織架構(gòu)到這些規(guī)律。這實質(zhì)上是壓縮成SIM簡單的信息是判斷MENT通話的壓縮是可以接受的。第四步是確定這說明一些變化。在現(xiàn)有的復(fù)雜系統(tǒng)的分析，這可能意味著分組已經(jīng)注意到的變化。在創(chuàng)建復(fù)雜的系統(tǒng)，這可以是故意，不同的元素。使用的模式是指驗證現(xiàn)實世界。最后，現(xiàn)實世界的壓力造成的圖式，在大多數(shù)情況下，最

42、有意義的選擇。</p><p>　　值得一提的是，在解釋這個周期，蓋爾曼博士[1994b]對生物系統(tǒng)，而不是集中在由人設(shè)計的系統(tǒng)，所以周期的人造復(fù)雜系統(tǒng)的適用性，是一個普遍的真理暗示。</p><p>　　300 謝爾德和莫斯塔沙瑞</p><p>　　圖2。系統(tǒng)比較復(fù)雜的系統(tǒng)。[彩色圖，可以被視為在網(wǎng)上發(fā)行，這是在www.interscience.wiley