模糊控制-外文資料翻譯

1、<p>　　畢業(yè)設(shè)計(jì)(論文)外文資料翻譯</p><p>　　注：請(qǐng)將該封面與附件裝訂成冊(cè)。附件1：外文資料翻譯譯文</p><p><b>　　模糊邏輯</b></p><p>　　歡迎進(jìn)入模糊邏輯的精彩世界，你可以用新科學(xué)有力地實(shí)現(xiàn)一些東西。在你的技術(shù)與管理技能的領(lǐng)域中，增加了基于模糊邏輯分析和控制的能力，你就可以實(shí)現(xiàn)除此之外的其

2、他人與物無(wú)法做到的事情。</p><p>　　以下就是模糊邏輯的基礎(chǔ)知識(shí)：隨著系統(tǒng)復(fù)雜性的增加，對(duì)系統(tǒng)精確的闡述變得越來(lái)越難，最終變得無(wú)法闡述。于是，終于到達(dá)了一個(gè)只有靠人類(lèi)發(fā)明的模糊邏輯才能解決的復(fù)雜程度。模糊邏輯用于系統(tǒng)的分析和控制設(shè)計(jì)，因?yàn)樗梢钥s短工程發(fā)展的時(shí)間；有時(shí)，在一些高度復(fù)雜的系統(tǒng)中，這是唯一可以解決問(wèn)題的方法。雖然，我們經(jīng)常認(rèn)為控制是和控制一個(gè)物理系統(tǒng)有關(guān)系的，但是，扎德博士最初設(shè)計(jì)這個(gè)概念的時(shí)

3、候本意并非如此。實(shí)際上，模糊邏輯適用于生物，經(jīng)濟(jì)，市場(chǎng)營(yíng)銷(xiāo)和其他大而復(fù)雜的系統(tǒng)。</p><p>　　模糊這個(gè)詞最早出現(xiàn)在扎德博士于1962年在一個(gè)工程學(xué)權(quán)威刊物上發(fā)表論文中。1963年，扎德博士成為加州大學(xué)伯克利分校電氣工程學(xué)院院長(zhǎng)。那就意味著達(dá)到了電氣工程領(lǐng)域的頂尖。扎德博士認(rèn)為模糊控制是那時(shí)的熱點(diǎn)，不是以后的熱點(diǎn)，更不應(yīng)該受到輕視。目前已經(jīng)有了成千上萬(wàn)基于模糊邏輯的產(chǎn)品，從聚焦照相機(jī)到可以根據(jù)衣服臟度自我控

4、制洗滌方式的洗衣機(jī)等。如果你在美國(guó)，你會(huì)很容易找到基于模糊的系統(tǒng)。想一想，當(dāng)通用汽車(chē)告訴大眾，她生產(chǎn)的汽車(chē)其反剎車(chē)是根據(jù)模糊邏輯而造成的時(shí)候，那會(huì)對(duì)其銷(xiāo)售造成多么大的影響。</p><p><b>　　以下的章節(jié)包括：</b></p><p>　　1)介紹處于商業(yè)等各個(gè)領(lǐng)域的人們他們?nèi)绻麖哪：壿嬔葑兌鴣?lái)的利益中得到好處，以及幫助大家理解模糊邏輯是怎么工作的。<

5、/p><p>　　2)提供模糊邏輯是怎么工作的一種指導(dǎo)，只有人們知道了這一點(diǎn)，才能運(yùn)用它用于做一些對(duì)自己有利的事情。</p><p>　　這本書(shū)就是一個(gè)指導(dǎo)，因此盡管你不是電氣領(lǐng)域的專(zhuān)家，你也可以運(yùn)用模糊邏輯。需要指出的是有一些針對(duì)模糊邏輯的相反觀點(diǎn)和批評(píng)。一個(gè)人應(yīng)該學(xué)會(huì)觀察反面的各個(gè)觀點(diǎn)，從而得出自己的觀點(diǎn)。我個(gè)人認(rèn)為，身為被表?yè)P(yáng)以及因?qū)戧P(guān)于模糊邏輯論文而受到贊賞的作者，他會(huì)認(rèn)為，在這個(gè)領(lǐng)域

6、中的這種批評(píng)有點(diǎn)過(guò)激。但是，請(qǐng)不要總相信我的觀點(diǎn)。你應(yīng)該耳聽(tīng)四方，然后做出自己的看法。</p><p>　　這一本書(shū)還未正式出版，如此“正直的簡(jiǎn)單人們” 能充分地了解模糊邏輯的觀念并且利用它, 或至少?zèng)Q定如果他們需要深深地深入在主題上存在的博士水平文學(xué)的很不錯(cuò)的主題。這一本書(shū)是引導(dǎo)者，因此，你能對(duì)模糊邏輯做某事,即使你不是一個(gè)專(zhuān)攻領(lǐng)域或一個(gè)先進(jìn)的數(shù)傳系統(tǒng)電子學(xué)工程師的博士。我們應(yīng)該被注意有論爭(zhēng)和關(guān)于模糊邏輯的批評(píng)

7、。一定要讀爭(zhēng)論的各種不同立場(chǎng)并且達(dá)成他們自己的結(jié)論。親自地，為他的關(guān)于模糊邏輯的寫(xiě)作，兩者都已經(jīng)被稱(chēng)贊而且辱罵, 感覺(jué)批評(píng)家是太硬的在他們的宇宙把握中并且 “不應(yīng)該那么做的”. 但是,為它大家可以不用在意我所說(shuō)的話(huà)。你一定看所有的立場(chǎng)而且組成你自己的思想。段落直接地在下面在一些短字中說(shuō),“什么是模糊邏輯”。但是，我們看看這一本書(shū)的余下部分和其他的相關(guān)文章，相信會(huì)對(duì)我們進(jìn)一步理解模糊邏輯有所幫助。</p><p>

8、　　假設(shè)你開(kāi)著車(chē)行駛在傳統(tǒng)的雙向道，6個(gè)車(chē)道的公路上，交通燈之間距離是1公里。車(chē)速限制在45M之內(nèi)，而最好的速度應(yīng)該在48M。你如何定義“遵守交通規(guī)則”呢？很難！但是，這卻是人類(lèi)經(jīng)常要做并且做的很好的事情。將會(huì)有一些車(chē)手的車(chē)速總是在48M前后，也有一些人的車(chē)速總是定在45M。實(shí)際上，大部分的人會(huì)將車(chē)速控制在48M，他們用的就是模糊推理。在交通中還存在著一系列此類(lèi)的案例。</p><p>　　你在城鎮(zhèn)中駕駛車(chē)輛的這

9、個(gè)模糊推理能力，也曾被我們的祖先用于獲得食物，衣服，骨具等。</p><p>　　人類(lèi)和外界的物理世界相接觸的時(shí)候，有能力吸納和分清從物理世界中得到的信息。并且綜合它們而得到最好的行為方式。所有的動(dòng)物都會(huì)這么做，只不過(guò)，人類(lèi)做的比較好，因此他成為了地球的主宰者。</p><p>　　你想一想，我們攝入的大部分信息都是不精確的。比如：汽車(chē)的沖刺速度。我們將這稱(chēng)為模糊輸入；但是也有一些是很合理

10、的，精確的輸入，比如：你的閱讀速度。我們稱(chēng)為模糊處理。模糊學(xué)理論家就會(huì)建議運(yùn)用所謂的模糊推理。</p><p>　　模糊邏輯是人腦工作的方式。我們可以將這移植到機(jī)器身上，所以，有時(shí)，機(jī)器具有了人腦的相似思維。模糊邏輯和分析系統(tǒng)可以使自然界中的電氣自動(dòng)化。比如經(jīng)濟(jì)數(shù)據(jù)等內(nèi)，人類(lèi)語(yǔ)言中總是含有：“如果-那么”的規(guī)則。</p><p>　　模糊邏輯分析和控制的過(guò)程是：</p>&l

11、t;p>　　1)接受一個(gè)或者多個(gè)我們希望去分析的數(shù)據(jù)量或者其他的變量。</p><p>　　2)綜合傳統(tǒng)的非模糊系統(tǒng)，用簡(jiǎn)便的“如果—那么”模式來(lái)表示，并將要處理的量進(jìn)行處理。</p><p>　　3)從由不同規(guī)則里得到的輸出結(jié)果中進(jìn)行平衡。得出的結(jié)果要求芯片如果工作。最后得到的就是一個(gè)不再是模糊而是精確的量。</p><p>　　模糊就是一種用于估算無(wú)法精確

12、測(cè)量的系統(tǒng)的概念。事實(shí)上，在宇宙中，人們?cè)u(píng)估任何事情都存在一定的模糊。不論你對(duì)某工具的測(cè)量是多么的精確，模糊概念始終是模糊邏輯中模糊分析和控制的基礎(chǔ)。</p><p>　　對(duì)模糊邏輯系統(tǒng)來(lái)說(shuō)，可測(cè)量的，非模糊的輸入數(shù)據(jù)是最主要的。例如：溫度傳感器檢測(cè)到的溫度，經(jīng)濟(jì)數(shù)據(jù)。人類(lèi)進(jìn)行模糊控制的時(shí)候，應(yīng)該將模糊轉(zhuǎn)化成為計(jì)算機(jī)可以識(shí)別的信號(hào)。我們將它的值域定在0到1之間。比如，房屋內(nèi)部的溫度是多少，人們可能定在0.2，如果

13、溫度處于零下，那么可能定為0.9甚至1。你可以看出來(lái)，這些就是模糊概念。通過(guò)模糊評(píng)估，值域定在0到1之間。這就給我們進(jìn)行模糊推理提供了一種規(guī)則，這樣，我們就可以完成控制工程。 </p><p>　　諾瓦瓷利用運(yùn)用模糊邏輯的電腦就可以打敗數(shù)學(xué)家們靠公式和傳統(tǒng)編程的控制器。模糊邏輯利用人們的一般思維；這種一般思維對(duì)一個(gè)新的系統(tǒng)來(lái)說(shuō)合情合理，并且對(duì)一個(gè)曾有人控制的系統(tǒng)來(lái)說(shuō)，它又能顯示出很有經(jīng)驗(yàn)。這里有一個(gè)

14、將人類(lèi)的一般思維運(yùn)用到一個(gè)控制系統(tǒng)的例子。元件產(chǎn)品的難度遠(yuǎn)遠(yuǎn)超出了你的想象。最后，他們將人類(lèi)的大量經(jīng)驗(yàn)通過(guò)“如果-那么”的規(guī)則輸入機(jī)器中。</p><p>　　模糊邏輯分析和控制的部件包括：物理控制，比如機(jī)器速度或者操作一個(gè)元件；經(jīng)濟(jì)和財(cái)政決策；心理情景；安全狀態(tài)以及其他一些改善產(chǎn)品的眾多例子。</p><p>　　這本書(shū)要探討的是模糊邏輯在控制機(jī)器，經(jīng)濟(jì)決策等方面的應(yīng)用。看起來(lái)，當(dāng)初，扎

15、德博士發(fā)明模糊邏輯時(shí)，想將它運(yùn)用到經(jīng)濟(jì)，政治等各個(gè)方面。</p><p>　　如果沒(méi)有個(gè)人電腦，就很難將模糊邏輯運(yùn)用于控制機(jī)器和其他一些地方。沒(méi)有了個(gè)人電腦的速度，就很難運(yùn)用人力控制機(jī)器以及具有足夠的持久力去控制機(jī)器。你用一臺(tái)內(nèi)含模糊邏輯的BASIC或者C++的個(gè)人電腦比用一臺(tái)其他的電腦更節(jié)省錢(qián)。編程邏輯控制器擁有了自己的地方，他們簡(jiǎn)單，可靠，并且維持著美國(guó)工業(yè)的運(yùn)轉(zhuǎn)。</p><p>　

16、　對(duì)于一個(gè)更為復(fù)雜的系統(tǒng)，最好的方法就是用電腦和模糊邏輯將系統(tǒng)組合，尤其當(dāng)一個(gè)非專(zhuān)業(yè)人士來(lái)主持重大工程項(xiàng)目的時(shí)候。</p><p>　　這是地球上智能生命里的一個(gè)里程碑：</p><p>　　在宇宙任何地方出現(xiàn)的智能生命，都可能應(yīng)用到模糊邏輯。它是一個(gè)廣泛的規(guī)則和概念。我們開(kāi)始認(rèn)識(shí)到在智能化的進(jìn)程中，定義和應(yīng)用模糊邏輯是一個(gè)重要的階段。在地球上，我們只是剛剛到達(dá)那個(gè)時(shí)刻，你需要知道并開(kāi)始應(yīng)

17、用模糊邏輯。</p><p>　　至今的爭(zhēng)論并沒(méi)有使我們適應(yīng)和理解模糊邏輯的大部分書(shū)籍和論文。因?yàn)?，那些作者大多是圓滑老練的。以下是一些可以幫助我們理解的解釋性語(yǔ)言。這些最早是由扎德博士發(fā)明模糊邏輯的時(shí)候建立的。</p><p>　　模糊—系統(tǒng)分析可以精確區(qū)分的模糊的程度。在這里我們不能稱(chēng)之為模糊，因?yàn)槭腔谝粋€(gè)人的觀點(diǎn)的。因此，模糊還是不模糊就和系統(tǒng)分析精確的程度有關(guān)。一個(gè)系統(tǒng)分析規(guī)則的

18、精確與一個(gè)人的模糊意念不相干。不如你有一個(gè)這樣的規(guī)則：如果氣壓上升到600P，那么關(guān)掉一切設(shè)備。這個(gè)規(guī)則就不是模糊的。</p><p>　　隨著系統(tǒng)復(fù)雜性的增加，對(duì)系統(tǒng)精確的闡述變得越來(lái)越難，最終變得無(wú)法闡述。于是，終于到達(dá)了一個(gè)只有靠人類(lèi)發(fā)明的模糊邏輯才能解決的復(fù)雜程度。</p><p>　　模糊集合—模糊集合幾乎存在于任何場(chǎng)合，比如：高的，矮的，速度快，慢等。我們給它們定了一個(gè)從0到1

19、的值域。例如，我遇到了一個(gè)6尺3寸的人，我認(rèn)為他是我見(jiàn)過(guò)的最高的人了。于是，我將值定位在0.98。</p><p>　　在一個(gè)模糊控制系統(tǒng)中，模糊集是以下列方法進(jìn)行的。以測(cè)量速度來(lái)作為例子。系統(tǒng)編程便會(huì)在“太快”和“無(wú)須改變”之間選擇，最終進(jìn)行反饋并將數(shù)據(jù)輸入系統(tǒng)當(dāng)中。這樣的情況我們?cè)谝韵碌恼鹿?jié)中將會(huì)有進(jìn)一步的談?dòng)憽?lt;/p><p>　　摘要信息—人們處理信息不是基于開(kāi)關(guān)的兩個(gè)端點(diǎn)，而是基

20、于模糊概念的。所有的輸入最后處理得到精確的數(shù)值輸出，這些可以指導(dǎo)人們進(jìn)行行動(dòng)。模糊邏輯控制系統(tǒng)的目的也是在此。</p><p>　　輸入的數(shù)據(jù)可能是極大的，但是人們可以處理它。操縱這些并最終變成人們可以執(zhí)行的輸出是人類(lèi)大腦的特有功能。這是人類(lèi)和電腦之間存在的一個(gè)重要特性。人們創(chuàng)造基于人工智能的電腦來(lái)挑戰(zhàn)人類(lèi)的這種能力，但是很難建造一種這樣的電腦。</p><p>　　模糊多樣化—一些概念如

21、紅色等，都是模糊的，他們都是基于人類(lèi)概念的，而不是精確的。這些詞就具有模糊多樣化。</p><p>　　語(yǔ)言多樣化—這些語(yǔ)言和我們平常用的語(yǔ)言有關(guān)聯(lián)。</p><p>　　速度是一種模糊多樣化。模糊多樣化變成語(yǔ)言多樣化，這是當(dāng)我們應(yīng)用語(yǔ)言去描述它的時(shí)候。比如：非?？欤瑯O慢等。語(yǔ)言多樣化最主要的功能就是，它可以處理那些靠公式等難以處理的復(fù)雜系統(tǒng)。語(yǔ)言多樣化在控制系統(tǒng)中帶有反饋的功能以及和其他

22、的狀態(tài)相聯(lián)系。比如：速度太快，則關(guān)掉加速器。</p><p>　　討論范圍—拿女人當(dāng)例子，如果我們談到女人，那么各個(gè)地方的女人都成了我們談?wù)摰膶?duì)象。討論范圍是一種將同類(lèi)的物質(zhì)組合在一起的概念。它是由模糊集合組成的。比如：女人的討論范圍是由專(zhuān)業(yè)女士，高的女人等組成的。</p><p>　　世界上的第一個(gè)模糊邏輯控制器。</p><p>　　1973年，英國(guó)倫敦大學(xué)的師

23、生正試圖穩(wěn)定一個(gè)先前制造的流動(dòng)動(dòng)力機(jī)，雖然，他們擁有各種不同的先進(jìn)物質(zhì)，但是卻無(wú)法按照自己的意愿來(lái)控制動(dòng)力機(jī)。它的速度不是太快，就是太慢，無(wú)法與其他器件相配套?，斶_(dá)尼教授讀了一篇扎德博士寫(xiě)的文章，扎德博士是加州大學(xué)伯克利分校電氣工程學(xué)院院長(zhǎng)。那就意味著達(dá)到了電氣工程領(lǐng)域的頂尖。他是模糊方面的權(quán)威，但是當(dāng)時(shí)有一些人以不同名義反對(duì)模糊概念?，斶_(dá)尼教授和他的學(xué)生決定用模糊邏輯來(lái)試一試。在周末，他們給自己的流動(dòng)動(dòng)力機(jī)安裝了世界上第一個(gè)模糊系統(tǒng)。

24、并且載入了歷史。這個(gè)模糊控制器運(yùn)行的相當(dāng)好，比以往他們用過(guò)的各個(gè)方案都要好。流動(dòng)動(dòng)力機(jī)運(yùn)行的速度控制的很好。正如你想的那樣，它運(yùn)行的不錯(cuò)，總是可以定在某個(gè)區(qū)域，不會(huì)抖動(dòng)并且總處于穩(wěn)定。這是科學(xué)發(fā)展歷史上一個(gè)令人興奮并且重要的時(shí)刻。</p><p>　　瑪達(dá)尼教授的模糊控制系統(tǒng)有四個(gè)輸入：溫度檢測(cè)偏差糾正，速度，氣壓等的糾正等。并且，這個(gè)系統(tǒng)有兩個(gè)輸出。他們是獨(dú)立工作的。</p><p>　

25、　要想制造一個(gè)模糊系統(tǒng)，我們無(wú)須上述瑪達(dá)尼教授的模糊控制系統(tǒng)中的持續(xù)反饋系統(tǒng)。你能從模糊邏輯文章中得到不少深刻的印象。一個(gè)模糊邏輯控制系統(tǒng)應(yīng)該簡(jiǎn)單成“如果摩托車(chē)的缸體溫度有點(diǎn)太高，那么就應(yīng)該關(guān)掉熱源如發(fā)動(dòng)機(jī)等。”或者“公司的老總和其他高層人士正在出售公司的股票，那么我們也應(yīng)該盡快賣(mài)掉”。</p><p>　　模糊邏輯系統(tǒng)無(wú)須變成一個(gè)電子機(jī)械系統(tǒng)。比如，模糊邏輯系統(tǒng)可以用于3千萬(wàn)美元和日元的兌換決策。模糊邏輯控制器

26、可以控制摩托車(chē)和其他的一些東西并進(jìn)行持續(xù)的反饋控制。</p><p>　　控制器典型的有多輸入和多輸出。在計(jì)算機(jī)中輸入了各個(gè)適當(dāng)?shù)某绦?，那么模糊邏輯控制器就可以進(jìn)行監(jiān)測(cè)和控制各種輸入。程序可以從一個(gè)任務(wù)跳轉(zhuǎn)到另外一個(gè)任務(wù)，程序獲得數(shù)據(jù)輸入并且向命令控制器發(fā)出指令。</p><p>　　向模糊邏輯控制系統(tǒng)輸入的各種輸入數(shù)據(jù)是由現(xiàn)實(shí)世界中得來(lái)的。向財(cái)政交易系統(tǒng)輸入的也是從人們的評(píng)估中得到的。&

27、lt;/p><p>　　模糊邏輯的進(jìn)步，從一開(kāi)始，模糊系統(tǒng)就是在不斷應(yīng)用和重要性中發(fā)展起來(lái)的，現(xiàn)在，這已經(jīng)是應(yīng)用廣泛的概念?；谀：壿嫷膫€(gè)人電腦是很迷人的。用從前的傳統(tǒng)方法是無(wú)法定義和解決這些問(wèn)題的。</p><p>　　一個(gè)系統(tǒng)是一個(gè)電子和機(jī)械的系統(tǒng)，它能使被控制系統(tǒng)的輸出能夠自動(dòng)地停留在操作者所預(yù)定的位置上。在你空調(diào)里面的溫度檢測(cè)器是一個(gè)控制系統(tǒng)。你車(chē)上的線路控制是一個(gè)控制系統(tǒng)?？刂瓶赡?/p>

28、是間斷的信號(hào)或者是持續(xù)的控制流。在日本，一個(gè)教授創(chuàng)立了一個(gè)可以控制直升飛機(jī)的模糊邏輯控制系統(tǒng)。而這是人類(lèi)直升飛機(jī)飛行員無(wú)法作到的。并且日本在這方面研究深入，建立了一個(gè)舒適的就像臥室里面通道一樣的地鐵。</p><p>　　在美國(guó)，模糊邏輯控制正在得到名聲, 但是不是同樣地廣泛地使用，像在日本一樣。日本賣(mài)被控制的照相機(jī)，洗衣機(jī)和更多的模糊邏輯。一個(gè)英特網(wǎng)搜尋引擎歸還超過(guò)16,000 頁(yè)，當(dāng)你搜尋的時(shí)候在模糊+邏輯。

29、被建立控制跟隨人類(lèi)"模糊"的式樣活動(dòng)的模糊邏輯的個(gè)人計(jì)算機(jī)。然而，人類(lèi)通常接受,處理而且有所反應(yīng)較多的輸入超過(guò)被建立模糊邏輯控制器的典型計(jì)算機(jī)。(這是不一定如此；一部在日本被建立模糊邏輯控制系統(tǒng)的計(jì)算機(jī)在財(cái)政的市場(chǎng)中交易并且利用 800 輸入)。模糊邏輯控制輸入-人類(lèi)和被建立模糊邏輯機(jī)器控制的計(jì)算機(jī)，計(jì)算機(jī)像人類(lèi)的模糊邏輯控制,但是當(dāng)計(jì)算機(jī)的輸入性質(zhì)被考慮的時(shí)候有一種不同的特性。人類(lèi)以模糊樣子評(píng)估來(lái)自他們的環(huán)境輸入,

30、然而機(jī)器/ 計(jì)算機(jī)獲得像 112 度 F 這樣的精確價(jià)值,以對(duì)數(shù)傳轉(zhuǎn)換器的一個(gè)轉(zhuǎn)換器和一個(gè)類(lèi)比獲得。計(jì)算機(jī)輸入會(huì)是計(jì)算機(jī)測(cè)定,讓我們說(shuō),112 度 F.人類(lèi)的輸入會(huì)是太溫暖的模糊感覺(jué)。人類(lèi)的發(fā)言權(quán)：雨水太熱 。計(jì)算機(jī)類(lèi)比輸入測(cè)量的結(jié)果說(shuō),雨水是我的計(jì)劃112度和“如果 - 然后”陳述告訴我水太溫暖.一個(gè)人類(lèi)的發(fā)言權(quán)：我見(jiàn)到二個(gè)高的人和一個(gè)短的。計(jì)算機(jī)說(shuō)：我測(cè)量二個(gè)人,6'6" 和 6</p><p&

31、gt;　　即使測(cè)量了輸入因?yàn)橛?jì)算機(jī)變得更精確, 轉(zhuǎn)換器源自輸入的點(diǎn)向前地我們?nèi)匀辉谀：壿嫹椒ǚ绞街惺褂盟麄冏窂奈覀兊哪：?人類(lèi)接近控制。對(duì)于一個(gè)人類(lèi)，如果陣雨水太溫暖,那么就準(zhǔn)備稍微使溫度下降下去。對(duì)于一部計(jì)算機(jī)，"如果 - 然后" 計(jì)畫(huà)的陳述會(huì)開(kāi)始以一個(gè)被提供的人類(lèi)為基礎(chǔ)的溫度降低人 “如果-然后”規(guī)則,藉由操作一個(gè)活瓣的指令輸出。</p><p>　　為了要產(chǎn)生一部被建立模糊邏輯控制系統(tǒng)

32、的個(gè)人計(jì)算機(jī)，我們：</p><p><b>　　1)決定輸入。</b></p><p>　　2)用 "描述因素和效果系統(tǒng)的行動(dòng)模糊規(guī)則"在簡(jiǎn)單的英文字中陳述。</p><p>　　3)寫(xiě)一個(gè)電腦程式給對(duì)輸入有所反應(yīng)而且決定輸出,分開(kāi)的考慮每個(gè)輸入。規(guī)則變成“如果-然后”計(jì)劃的陳述。(當(dāng)將會(huì)在下面被見(jiàn)到之時(shí), 回應(yīng)使控制成環(huán)

33、哪里被牽涉,圖解式三角形的使用能幫助看得見(jiàn)而且計(jì)算這個(gè)輸入- 輸出的行動(dòng))。</p><p>　　4)在計(jì)劃中，使用被重量的平均合并進(jìn)入不同的輸出在受約束的系統(tǒng)方面的演戲之內(nèi)被個(gè)別的輸入要求的各種不同的行動(dòng)(在事件中只有輸出, 然后合并不是必需的,當(dāng)作需要的唯一計(jì)數(shù)輸出)。</p><p>　　模糊邏輯方式概念化并實(shí)現(xiàn)控制系統(tǒng)是比較容易。程序被轉(zhuǎn)為一系列 visualizable 步驟。這

34、是非常重要的一點(diǎn)。實(shí)際上實(shí)現(xiàn)一個(gè)控制系統(tǒng), 甚至一個(gè)簡(jiǎn)單的控制系統(tǒng), 出現(xiàn)也很困難。料想不到的越軌和實(shí)際的反常事物不可避免發(fā)生。得到正確地工作的程序最后作為一個(gè)削減和嘗試努力。</p><p>　　在工業(yè)中讀關(guān)于模糊邏輯控制應(yīng)用的有關(guān)方面，突出的重要點(diǎn)之一是: 因?yàn)樗坦こ贪l(fā)展的時(shí)間，所以模糊邏輯被用。模糊邏輯使工程師能夠不需要廣泛的實(shí)驗(yàn)就很快地配置系統(tǒng)并且利用來(lái)自用手已經(jīng)表演工作的專(zhuān)家人類(lèi)的操作員的數(shù)據(jù)。也許

35、超過(guò)飛的直升飛機(jī)或流動(dòng)的地下鐵更下來(lái)對(duì)地球你的控制需要是某事很多。也許全部你想要做是生計(jì)你的平滑地跑的小生意鋸木廠，藉由木材變更和變更的刀鋒銳利。也許你操作一個(gè)天然氣壓縮物，因?yàn)橐恍┕ぞ呖偸怯砍瞿鞘艿皆谥系挠绊懚冶?而且你需要有壓縮物自動(dòng)地為了要低下地停留在線上而且保存吸強(qiáng)迫拿最適宜的制造,調(diào)整。也許你夢(mèng)到一輛會(huì)自動(dòng)地調(diào)整的比賽汽車(chē)到變更情況,像上述的直升飛機(jī)對(duì)沒(méi)有轉(zhuǎn)子刀鋒的存在調(diào)整一樣的有效地的保持最適宜的裝備。</p&g

36、t;<p>　　那有一個(gè)百萬(wàn)個(gè)故事，而且我們不能夠猜測(cè)什么是你的故事，但是機(jī)會(huì)是, 如果那里是某種你想要控制的，而且你不是富有經(jīng)驗(yàn)的專(zhuān)業(yè)人士和有數(shù)百萬(wàn)元供給經(jīng)費(fèi)的公司工程師, 然后模糊邏輯可能為你做到那些。如果你真的處于這種情況, 它仍然可能適用于你。在技術(shù)的世界中一些最好的思想家試著解釋模糊邏輯為什么工作。對(duì)我們這些平常的人,事實(shí)是模糊邏輯確實(shí)工作, 似乎更有效率于許多貴的和復(fù)雜的系統(tǒng)并且是可以理解的和能負(fù)擔(dān)的。<

37、/p><p>　　附件2：外文原文（復(fù)印件）</p><p>　　Fuzzy Logic</p><p>　　Welcome to the wonderful world of fuzzy logic, the new science you can use to powerfully get things done. Add the ability to utiliz

38、e personal computer based fuzzy logic analysis and control to your technical and management skills and you can do things that humans and machines cannot otherwise do. </p><p>　　Following is the base on which

39、 fuzzy logic is built: As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the f

40、uzzy logic method born in humans is the only way to get at the problem. Fuzzy logic is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of hi

41、ghly complex systems, is the o</p><p>　　The term "fuzzy" was first used by Dr. Lotfi Zadeh in the engineering journal, "Proceedings of the IRE," a leading engineering journal, in 1962. Dr

42、. Zadeh became, in 1963, the Chairman of the Electrical Engineering department of the University of California at Berkeley. That is about as high as you can go in the electrical engineering field. Dr. Zadeh thoughts are

43、not to be taken lightly. Fuzzy logic is not the wave of the future. It is now! There are already hundreds of millions of dollars of s</p><p>　　Objectives of the following chapters include: </p><p&

44、gt;　　1)To introduce to individuals in the fields of business, industry, science, invention and day-to-day living the power and benefits available to them through the fuzzy logic method and to help them understand how fuz

45、zy logic works.   </p><p>　　2)To provide a fuzzy logic "how-to-do-it" guide, in terms everyone can understand, so everyone can put fuzzy logic to work doing something useful for them.   &

46、lt;/p><p>　　This book is being written so "Just Plain Folks" can understand the concept of fuzzy logic sufficiently to utilize it, or to at least determine if they need to dig deeply into the subject

47、in the great quantity of Ph.D. level literature existing on the subject. This book is a guide, so you can do something with fuzzy logic, even if you are not a Ph.D. specializing in the field or an advanced digital system

48、s electronics engineer. It should be noted there is controversy and criticism regarding fuzz</p><p>　　Suppose you are driving down a typical, two way, 6 lane street in a large city, one mile between signal l

49、ights. The speed limit is posted at 45 Mph. It is usually optimum and safest to "drive with the traffic," which will usually be going about 48 Mph. How do you define with specific, precise instructions "dr

50、iving with the traffic?" It is difficult.  But, it is the kind of thing humans do every day and do well. There will be some drivers weaving in and out and going more than 48 Mph and a few dr</p><p>

51、;　　The same ability you have to drive down a modern city street was used by our ancestors to successfully organize and carry out chases to drive wooly mammoths into pits, to obtain food, clothing and bone tools.   &

52、lt;/p><p>　　Human beings have the ability to take in and evaluate all sorts of information from the physical world they are in contact with and to mentally analyze, average and summarize all this input data int

53、o an optimum course of action. All living things do this, but humans do it more and do it better and have become the dominant species of the planet.   </p><p>　　If you think about it, much of the inform

54、ation you take in is not very precisely defined, such as the speed of a vehicle coming up from behind. We call this fuzzy input. However, some of your "input" is reasonably precise and non-fuzzy such as the spe

55、edometer reading. Your processing of all this information is not very precisely definable. We call this fuzzy processing. Fuzzy logic theorists would call it using fuzzy algorithms (algorithm is another word for procedur

56、e or program, as in a compute</p><p>　　The fuzzy logic analysis and control method is, therefore: </p><p>　　1)Receiving of one, or a large number, of measurement or other assessment of condition

57、s existing in some system we wish to analyze or control.</p><p>　　2)Processing all these inputs according to human based, fuzzy "If-Then" rules, which can be expressed in plain language words, in c

58、ombination with traditional non-fuzzy processing.</p><p>　　3)Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which decides what to do or

59、tells a controlled system what to do. The output signal eventually arrived at is a precise appearing defuzzified, "crisp" value.  </p><p>　　Measured, non-fuzzy data is the primary input for t

60、he fuzzy logic method. Examples: temperature measured by a temperature transducer, motor speed, economic data, financial markets data, etc. It would not be usual in an electro-mechanical control system or a financial or

61、economic analysis system, but humans with their fuzzy perceptions could also provide input. There could be a human "in-the-loop." In the fuzzy logic literature, you will see the term "fuzzy set." 

62、0;A fuzzy set is a group of anythi</p><p>　　Novices using personal computers and the fuzzy logic method can beat Ph.D. mathematicians using formulas and conventional programmable logic controllers. Fuzzy log

63、ic makes use of human common sense. This common sense is either applied from what seems reasonable, for a new system, or from experience, for a system that has previously had a human operator. Here is an example of conve

64、rting human experience for use in a control system: I read of an attempt to automate a cement manufacturing operation</p><p>　　This book will talk about fuzzy logic in control applications - controlling mach

65、ines, physical conditions, processing plants, etc. It should be noted that when Dr. Zadeh invented fuzzy logic, it appears he had in mind applying fuzzy logic in many applications in addition to controlling machines, suc

66、h as economics, politics, biology, etc. Thank You Wozniak (Apple Computer), Jobs (Apple Computer), Gates (Microsoft) and Ed Roberts (the MITS, Altair entrepreneur) for the Personal Computer. </p><p>　　Withou

67、t personal computers, it would be difficult to use fuzzy logic to control machines and production plants, or do other analyses. Without the speed and versatility of the personal computer, we would never undertake the lab

68、orious and time consuming tasks of fuzzy logic based analyses and we could not handle the complexity, speed requirement and endurance needed for machine control. You can do far more with a simple fuzzy logic BASIC or C++

69、 program in a personal computer running in conjunction</p><p>　　For a more complicated system control application, an optimum solution may be patching things together with a personal computer and fuzzy logic

70、 rules, especially if the project is being done by someone who is not a professional, control systems engineer. </p><p>　　A Milestone Passed for Intelligent Life On Earth。If intelligent life has appeared any

71、where in the universe, "they" are probably using fuzzy logic. It is a universal principle and concept. Becoming aware of, defining and starting to use fuzzy logic is an important moment in the development of an

72、 intelligent civilization. On earth, we have just arrived at that important moment. You need to know and begin using fuzzy logic.   </p><p>　　The discussion so far does not adequately prepare us for rea

73、ding and understanding most books and articles about fuzzy logic, because of the terminology used by sophisticated authors. Following are explanations of some terms which should help in this regard. This terminology was

74、initially established by Dr. Zadeh when he originated the fuzzy logic concept.   </p><p>　　Fuzzy - The degree of fuzziness of a system analysis rule can vary between being very precise, in which case we

75、 would not call it "fuzzy", to being based on an opinion held by a human, which would be "fuzzy." Being fuzzy or not fuzzy, therefore, has to do with the degree of precision of a system analysis rule.

76、 A system analysis rule need not be based on human fuzzy perception. For example, you could have a rule, "If the boiler pressure rises to a danger point of 600 P as measured by a pressure t</p><p>　　Pri

77、nciple of Incompatibility (previously stated; repeated here) – </p><p>　　As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its beh

78、avior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem. </p><p>　　Fuzzy Sets - A fuzzy set is almost any condition for which we

79、have words: short men, tall women, hot, cold, new buildings, accelerator setting, ripe bananas, high intelligence, speed, weight, spongy, etc., where the condition can be given a value between 0 and 1. Example: A woman i

80、s 6 feet, 3 inches tall. In my experience, I think she is one of the tallest women I have ever met, so I rate her height at .98. This line of reasoning can go on indefinitely rating a great number of things between 0 a&l

81、t;/p><p>　　In fuzzy logic method control systems, degree of membership is used in the following way. A measurement of speed, for example, might be found to have a degree of membership in "too fast of"

82、 .6 and a degree of membership in "no change needed" of .2. The system program would then calculate the center of mass between "too fast" and "no change needed" to determine feedback action

83、to send to the input of the control system. This is discussed in more detail in subsequent chapters. Summarizing Informat</p><p>　　The input may be large masses of data, but humans can handle it. The ability

84、 to manipulate fuzzy sets and the subsequent summarizing capability to arrive at an output we can act on is one of the greatest assets of the human brain. This characteristic is the big difference between humans and digi

85、tal computers. Emulating this human ability is the challenge facing those who would create computer based artificial intelligence. It is proving very, very difficult to program a computer to have human-li</p><

86、p>　　Fuzzy Variable - Words like red, blue, etc., are fuzzy and can have many shades and tints. They are just human opinions, not based on precise measurement in angstroms. These words are fuzzy variables.</p>&

87、lt;p>　　If, for example, speed of a system is the attrribute being evaluated by fuzzy, "fuzzy" rules, then "speed" is a fuzzy variable. </p><p>　　Linguistic Variable - Linguistic means

88、relating to language, in our case plain language words. </p><p>　　Speed is a fuzzy variable. Accelerator setting is a fuzzy variable. Examples of linguistic variables are: somewhat fast speed, very high spee

89、d, real slow speed, excessively high accelerator setting, accelerator setting about right, etc. A fuzzy variable becomes a linguistic variable when we modify it with descriptive words, such as somewhat fast, very high, r

90、eal slow, etc. The main function of linguistic variables is to provide a means of working with the complex systems mentioned above as being</p><p>　　Universe of Discourse - Let us make women the object of ou

91、r consideration. All the women everywhere would be the universe of women. If we choose to discourse about (talk about) women, then all the women everywhere would be our Universe of Discourse. Universe of Discourse then,

92、is a way to say all the objects in the universe of a particular kind, usually designated by one word, that we happen to be talking about or working with in a fuzzy logic solution. A Universe of Discourse is made up of fu

93、zz</p><p>　　The World's First Fuzzy Logic Controller，In England in 1973 at the University of London, a professor and student were trying to stabilize the speed of a small steam engine the student had bui

94、lt. They had a lot going for them, sophisticated equipment like a PDP-8 minicomputer and conventional digital control equipment. But, they could not control the engine as well as they wanted. Engine speed would either ov

95、ershoot the target speed and arrive at the target speed after a series of oscillations, o</p><p>　　The professor, E. Mamdani, had read of a control method proposed by Dr. Lotfi Zadeh, head of the electrical

96、engineering department at the University of California at Berkeley, in the United States. Dr. Zadeh is the originator of the designation "fuzzy", which everyone suspects was selected to throw a little "pie

97、 in the face" of his more orthodox engineering colleagues, some of whom strongly opposed the fuzzy logic concept under any name.</p><p>　　Professor Mamdani and the student, S. Assilian, decided to give

98、fuzzy logic a try.   They spent a weekend setting their steam engine up with the world's first ever fuzzy logic control system ....... and went directly into the history books by harnessing the power of a force

99、in use by humans for 3 million years, but never before defined and used for the control of machines. The controller worked right away, and worked better than anything they had done with any other method.  The steam

100、engine spe</p><p>　　As you can see, the speed approached the desired value very quickly, did not overshoot and remained stable. It was an exciting and important moment in the history of scientific developmen

101、t. The Mamdani project made use of four inputs: boiler pressure error (how many temperature degrees away from the set point), rate of change of boiler pressure error, engine speed error and rate of change of engine speed