模糊邏輯外文資料翻譯

1、<p><b>　　淮 陰 工 學(xué) 院</b></p><p>　　畢業(yè)設(shè)計(論文)外文資料翻譯</p><p>　　注：請將該封面與附件裝訂成冊。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)

2、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)除此之外的其他人與物無法做到的事情。</p><p>　　以下就是模糊邏輯的基礎(chǔ)知識：隨著系統(tǒng)復(fù)雜性的增加，對系統(tǒng)精確的闡述變得越來越難，最終變得無法闡述。于是，終于到達(dá)了一個只有靠人類發(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，因?yàn)樗梢钥s短工程發(fā)展的時間；有時，在一些高度復(fù)雜的系統(tǒng)中，這是唯一可以

3、解決問題的方法。雖然，我們經(jīng)常認(rèn)為控制是和控制一個物理系統(tǒng)有關(guān)系的，但是，扎德博士最初設(shè)計這個概念的時候本意并非如此。實(shí)際上，模糊邏輯適用于生物，經(jīng)濟(jì)，市場營銷和其他大而復(fù)雜的系統(tǒng)。</p><p>　　模糊這個詞最早出現(xiàn)在扎德博士于1962年在一個工程學(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。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)為模糊控制是那時的熱點(diǎn)，不是以后

4、的熱點(diǎn)，更不應(yīng)該受到輕視。目前已經(jīng)有了成千上萬基于模糊邏輯的產(chǎn)品，從聚焦照相機(jī)到可以根據(jù)衣服臟度自我控制洗滌方式的洗衣機(jī)等。如果你在美國，你會很容易找到基于模糊的系統(tǒng)。想一想，當(dāng)通用汽車告訴大眾，她生產(chǎn)的汽車其反剎車是根據(jù)模糊邏輯而造成的時候，那會對其銷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

5、個領(lǐng)域的人們他們?nèi)绻麖哪：壿嬔葑兌鴣淼睦嬷械玫胶锰?，以及幫助大家理解模糊邏輯是怎么工作的?lt;/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)用它用于做一些對自己有利的事情。</p><p>　　這本書就是一個指導(dǎo)，因此盡管你不是電氣領(lǐng)域的專家，你也可以運(yùn)用模糊邏輯。需要指出的是有一些針對模糊邏輯的相反觀點(diǎn)和批評。一個人應(yīng)該學(xué)會觀察反面的各個觀點(diǎn)，從

6、而得出自己的觀點(diǎn)。我個人認(rèn)為，身為被表揚(yáng)以及因?qū)戧P(guān)于模糊邏輯論文而受到贊賞的作者，他會認(rèn)為，在這個領(lǐng)域中的這種批評有點(diǎn)過激。但是，請不要總相信我的觀點(diǎn)。你應(yīng)該耳聽四方，然后做出自己的看法。</p><p>　　這一本書還未正式出版，如此“正直的簡單人們” 能充分地了解模糊邏輯的觀念并且利用它, 或至少決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)的很不錯的主題。這一本書是引導(dǎo)者，因此，你能對模糊邏輯做某事,

7、即使你不是一個專攻領(lǐng)域或一個先進(jìn)的數(shù)傳系統(tǒng)電子學(xué)工程師的博士。我們應(yīng)該被注意有論爭和關(guān)于模糊邏輯的批評。一定要讀爭論的各種不同立場并且達(dá)成他們自己的結(jié)論。親自地，為他的關(guān)于模糊邏輯的寫作，兩者都已經(jīng)被稱贊而且辱罵, 感覺批評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的話。你一定看所有的立場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,“什么是模糊邏輯”。但是，我們看看這一本書的余下部

8、分和其他的相關(guān)文章，相信會對我們進(jìn)一步理解模糊邏輯有所幫助。</p><p>　　假設(shè)你開著車行駛在傳統(tǒng)的雙向道，6個車道的公路上，交通燈之間距離是1公里。車速限制在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，這卻是人類經(jīng)常要做并且做的很好的事情。將會有一些車手的車速總是在48M前后，也有一些人的車速總是定在45M。實(shí)際上，大部分的人會將車速控制在48M，他們用的就是模糊推理。

9、在交通中還存在著一系列此類的案例。</p><p>　　你在城鎮(zhèn)中駕駛車輛的這個模糊推理能力，也曾被我們的祖先用于獲得食物，衣服，骨具等。</p><p>　　人類和外界的物理世界相接觸的時候，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。所有的動物都會這么做，只不過，人類做的比較好，因此他成為了地球的主宰者。</p><p>　　你想一

10、想，我們攝入的大部分信息都是不精確的。比如：汽車的沖刺速度。我們將這稱為模糊輸入；但是也有一些是很合理的，精確的輸入，比如：你的閱讀速度。我們稱為模糊處理。模糊學(xué)理論家就會建議運(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，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化。比如經(jīng)濟(jì)數(shù)據(jù)等內(nèi)，人類語言中總是含有：“如果-那么”的規(guī)則。

11、</p><p>　　模糊邏輯分析和控制的過程是：</p><p>　　1)接受一個或者多個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)，用簡便的“如果—那么”模式來表示，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

12、一個不再是模糊而是精確的量。</p><p>　　模糊就是一種用于估算無法精確測量的系統(tǒng)的概念。事實(shí)上，在宇宙中，人們評估任何事情都存在一定的模糊。不論你對某工具的測量是多么的精確，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>　　對模糊邏輯系統(tǒng)來說，可測量的，非模糊的輸入數(shù)據(jù)是最主要的。例如：溫度傳感器檢測到的溫度，經(jīng)濟(jì)數(shù)據(jù)。人類進(jìn)行模糊控制的時候，應(yīng)該將模糊轉(zhuǎn)化成為計

13、算機(jī)可以識別的信號。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，如果溫度處于零下，那么可能定為0.9甚至1。你可以看出來，這些就是模糊概念。通過模糊評估，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

14、種一般思維對一個新的系統(tǒng)來說合情合理，并且對一個曾有人控制的系統(tǒng)來說，它又能顯示出很有經(jīng)驗(yàn)。這里有一個將人類的一般思維運(yùn)用到一個控制系統(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，他們將人類的大量經(jīng)驗(yàn)通過“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元件；經(jīng)濟(jì)和財政決策；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>

15、;<p>　　這本書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?？雌饋恚?dāng)初，扎德博士發(fā)明模糊邏輯時，想將它運(yùn)用到經(jīng)濟(jì)，政治等各個方面。</p><p>　　如果沒有個人電腦，就很難將模糊邏輯運(yùn)用于控制機(jī)器和其他一些地方。沒有了個人電腦的速度，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。你用一臺內(nèi)含模糊邏輯的BASIC或者C++的個人電腦比用一臺其他的電腦更節(jié)省錢。編程邏輯控制器擁

16、有了自己的地方，他們簡單，可靠，并且維持著美國工業(yè)的運(yùn)轉(zhuǎn)。</p><p>　　對于一個更為復(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)一個非專業(yè)人士來主持重大工程項目的時候。</p><p>　　這是地球上智能生命里的一個里程碑：</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。它是一個廣泛的規(guī)則和概念。我們開始認(rèn)識到在智能

17、化的進(jìn)程中，定義和應(yīng)用模糊邏輯是一個重要的階段。在地球上，我們只是剛剛到達(dá)那個時刻，你需要知道并開始應(yīng)用模糊邏輯。</p><p>　　至今的爭論并沒有使我們適應(yīng)和理解模糊邏輯的大部分書籍和論文。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言。這些最早是由扎德博士發(fā)明模糊邏輯的時候建立的。</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

18、之為模糊，因?yàn)槭腔谝粋€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)。一個系統(tǒng)分析規(guī)則的精確與一個人的模糊意念不相干。不如你有一個這樣的規(guī)則：如果氣壓上升到600P，那么關(guān)掉一切設(shè)備。這個規(guī)則就不是模糊的。</p><p>　　隨著系統(tǒng)復(fù)雜性的增加，對系統(tǒng)精確的闡述變得越來越難，最終變得無法闡述。于是，終于到達(dá)了一個只有靠人類發(fā)明的模糊邏輯才能解決的復(fù)雜程度。</p><p>

19、;　　模糊集合—模糊集合幾乎存在于任何場合，比如：高的，矮的，速度快，慢等。我們給它們定了一個從0到1的值域。例如，我遇到了一個6尺3寸的人，我認(rèn)為他是我見過的最高的人了。于是，我將值定位在0.98。</p><p>　　在一個模糊控制系統(tǒng)中，模糊集是以下列方法進(jìn)行的。以測量速度來作為例子。系統(tǒng)編程便會在“太快”和“無須改變”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。這樣的情況我們在以下的章節(jié)中將會有進(jìn)一步的談

20、討。</p><p>　　摘要信息—人們處理信息不是基于開關(guān)的兩個端點(diǎn)，而是基于模糊概念的。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。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)行的輸出是人類大腦的特有功能。這是人類和電腦之間存在的一個重要特性。人們創(chuàng)造基于人工智能的電腦來挑戰(zhàn)人類的這種

21、能力，但是很難建造一種這樣的電腦。</p><p>　　模糊多樣化—一些概念如紅色等，都是模糊的，他們都是基于人類概念的，而不是精確的。這些詞就具有模糊多樣化。</p><p>　　語言多樣化—這些語言和我們平常用的語言有關(guān)聯(lián)。</p><p>　　速度是一種模糊多樣化。模糊多樣化變成語言多樣化，這是當(dāng)我們應(yīng)用語言去描述它的時候。比如：非?？?，極慢等。語言多樣化最主

22、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)。語言多樣化在控制系統(tǒng)中帶有反饋的功能以及和其他的狀態(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，那么各個地方的女人都成了我們談?wù)摰膶ο蟆Ｓ懻摲秶且环N將同類的物質(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業(yè)女士，高的女人等組成的。</p><p>　　世

23、界上的第一個模糊邏輯控制器。</p><p>　　1973年，英國倫敦大學(xué)的師生正試圖穩(wěn)定一個先前制造的流動動力機(jī)，雖然，他們擁有各種不同的先進(jìn)物質(zhì)，但是卻無法按照自己的意愿來控制動力機(jī)。它的速度不是太快，就是太慢，無法與其他器件相配套?，斶_(dá)尼教授讀了一篇扎德博士寫的文章，扎德博士是加州大學(xué)伯克利分校電氣工程學(xué)院院長。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)時有一些人以不同名義反對模糊概念。

24、瑪達(dá)尼教授和他的學(xué)生決定用模糊邏輯來試一試。在周末，他們給自己的流動動力機(jī)安裝了世界上第一個模糊系統(tǒng)。并且載入了歷史。這個模糊控制器運(yùn)行的相當(dāng)好，比以往他們用過的各個方案都要好。流動動力機(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，總是可以定在某個區(qū)域，不會抖動并且總處于穩(wěn)定。這是科學(xué)發(fā)展歷史上一個令人興奮并且重要的時刻。</p><p>　　瑪達(dá)尼教授的模糊控制系統(tǒng)有四個輸入：溫度檢測偏差糾正，速度，氣

25、壓等的糾正等。并且，這個系統(tǒng)有兩個輸出。他們是獨(dú)立工作的。</p><p>　　要想制造一個模糊系統(tǒng)，我們無須上述瑪達(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。一個模糊邏輯控制系統(tǒng)應(yīng)該簡單成“如果摩托車的缸體溫度有點(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)該盡快賣掉”。</p><p>　　模糊

26、邏輯系統(tǒng)無須變成一個電子機(jī)械系統(tǒng)。比如，模糊邏輯系統(tǒng)可以用于3千萬美元和日元的兌換決策。模糊邏輯控制器可以控制摩托車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。在計算機(jī)中輸入了各個適當(dāng)?shù)某绦?，那么模糊邏輯控制器就可以進(jìn)行監(jiān)測和控制各種輸入。程序可以從一個任務(wù)跳轉(zhuǎn)到另外一個任務(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

27、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)世界中得來的。向財政交易系統(tǒng)輸入的也是從人們的評估中得到的。</p><p>　　模糊邏輯的進(jìn)步，從一開始，模糊系統(tǒng)就是在不斷應(yīng)用和重要性中發(fā)展起來的，現(xiàn)在，這已經(jīng)是應(yīng)用廣泛的概念?；谀：壿嫷膫€人電腦是很迷人的。用從前的傳統(tǒng)方法是無法定義和解決這些問題的。</p><p>　　一個系統(tǒng)是一個電子和機(jī)械的系統(tǒng)，它能使被控制系統(tǒng)的輸出能夠自動地停留在

28、操作者所預(yù)定的位置上。在你空調(diào)里面的溫度檢測器是一個控制系統(tǒng)。你車上的線路控制是一個控制系統(tǒng)。控制可能是間斷的信號或者是持續(xù)的控制流。在日本，一個教授創(chuàng)立了一個可以控制直升飛機(jī)的模糊邏輯控制系統(tǒng)。而這是人類直升飛機(jī)飛行員無法作到的。并且日本在這方面研究深入，建立了一個舒適的就像臥室里面通道一樣的地鐵。</p><p>　　在美國，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。日本賣被控制的照

29、相機(jī)，洗衣機(jī)和更多的模糊邏輯。一個英特網(wǎng)搜尋引擎歸還超過16,000 頁，當(dāng)你搜尋的時候在模糊+邏輯。被建立控制跟隨人類"模糊"的式樣活動的模糊邏輯的個人計算機(jī)。然而，人類通常接受,處理而且有所反應(yīng)較多的輸入超過被建立模糊邏輯控制器的典型計算機(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)的計算機(jī)在財政的市場中交易并且利用 800 輸入)。模糊邏輯控制輸入-人類和被建立模糊邏輯機(jī)器控制的計算機(jī)，計算機(jī)像人類的模

30、糊邏輯控制,但是當(dāng)計算機(jī)的輸入性質(zhì)被考慮的時候有一種不同的特性。人類以模糊樣子評估來自他們的環(huán)境輸入,然而機(jī)器/ 計算機(jī)獲得像 112 度 F 這樣的精確價值,以對數(shù)傳轉(zhuǎn)換器的一個轉(zhuǎn)換器和一個類比獲得。計算機(jī)輸入會是計算機(jī)測定,讓我們說,112 度 F.人類的輸入會是太溫暖的模糊感覺。人類的發(fā)言權(quán)：雨水太熱 。計算機(jī)類比輸入測量的結(jié)果說,雨水是我的計劃112度和“如果 - 然后”陳述告訴我水太溫暖.一個人類的發(fā)言權(quán)：我見到二個高的人和一

31、個短的。計算機(jī)說：我測量二個人,6'6" 和 6</p><p>　　即使測量了輸入因?yàn)橛嬎銠C(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：?人類接近控制。對于一個人類，如果陣雨水太溫暖,那么就準(zhǔn)備稍微使溫度下降下去。對于一部計算機(jī)，"如果 - 然后" 計畫的陳述會開始以一個被提供的人類為基礎(chǔ)的溫度降低人 “如果-然后”規(guī)則,藉由操作

32、一個活瓣的指令輸出。</p><p>　　為了要產(chǎn)生一部被建立模糊邏輯控制系統(tǒng)的個人計算機(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)的行動模糊規(guī)則"在簡單的英文字中陳述。</p><p>　　3)寫一個電腦程式給對輸入有所反應(yīng)而且決定

33、輸出,分開的考慮每個輸入。規(guī)則變成“如果-然后”計劃的陳述。(當(dāng)將會在下面被見到之時, 回應(yīng)使控制成環(huán)哪里被牽涉,圖解式三角形的使用能幫助看得見而且計算這個輸入- 輸出的行動)。</p><p>　　4)在計劃中，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)被個別的輸入要求的各種不同的行動(在事件中只有輸出, 然后合并不是必需的,當(dāng)作需要的唯一計數(shù)輸出)。</p><p>

34、;　　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。這是非常重要的一點(diǎn)。實(shí)際上實(shí)現(xiàn)一個控制系統(tǒng), 甚至一個簡單的控制系統(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)展的時間，所以模糊邏輯被用。模糊邏輯

35、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)并且利用來自用手已經(jīng)表演工作的專家人類的操作員的數(shù)據(jù)。也許超過飛的直升飛機(jī)或流動的地下鐵更下來對地球你的控制需要是某事很多。也許全部你想要做是生計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。也許你夢到一輛會自動地調(diào)整的比賽汽

36、車到變更情況,像上述的直升飛機(jī)對沒有轉(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><p>　　那有一個百萬個故事，而且我們不能夠猜測什么是你的故事，但是機(jī)會是, 如果那里是某種你想要控制的，而且你不是富有經(jīng)驗(yàn)的專業(yè)人士和有數(shù)百萬元供給經(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。對我們這些平常

37、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。</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 po

38、werfully get things done. Add the ability to utilize 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. &l

39、t;/p><p>　　Following is the base on which 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, event

40、ually arriving at a point of complexity where the fuzzy 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 engin

41、eering development and sometimes, in the case of highly complex systems, is the o</p><p>　　The term "fuzzy" was first used by Dr. Lotfi Zadeh in the engineering journal, "Proceedings of the IR

42、E," a leading engineering journal, in 1962. Dr. 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 el

43、ectrical engineering field. Dr. Zadeh thoughts are 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

44、 the following chapters include: </p><p>　　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 fuz

45、zy logic method and to help them understand how fuzzy 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 logi

46、c to work doing something useful for them.   </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 det

47、ermine if they need to dig deeply into the subject 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. spec

48、ializing in the field or an advanced digital systems 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 lan

49、e street in a large city, one mile between signal lights. 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

50、define with specific, precise instructions "driving 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 goin

51、g more than 48 Mph and a few dr</p><p>　　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

52、, to obtain food, clothing and bone tools.   </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 ana

53、lyze, average and summarize all this input data into 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>&

54、lt;p>　　If you think about it, much of the information 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"

55、is reasonably precise and non-fuzzy such as the speedometer 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

56、 algorithms (algorithm is another word for procedure 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 numb

57、er, of measurement or other assessment of conditions 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, w

58、hich can be expressed in plain language words, in combination with traditional non-fuzzy processing.</p><p>　　3)Averaging and weighting the resulting outputs from all the individual rules into one single out

59、put decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing defuzzified, "crisp" value.  </p><p>　　

60、Measured, non-fuzzy data is the primary input for the 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 e

61、lectro-mechanical control system or a financial or 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

62、, you will see the term "fuzzy set."  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 con

63、ventional programmable logic controllers. Fuzzy logic 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 previousl

64、y had a human operator. Here is an example of converting 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 fuz

65、zy logic in control applications - controlling machines, 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 ap

66、plications in addition to controlling machines, such as economics, politics, biology, etc. Thank You Wozniak (Apple Computer), Jobs (Apple Computer), Gates (Microsoft) and Ed Roberts (the MITS, Altair entrepreneur) for t

67、he Personal Computer. </p><p>　　Without 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

68、personal computer, we would never undertake the laborious 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

69、 do far more with a simple fuzzy logic BASIC or C++ program in a personal computer running in conjunction</p><p>　　For a more complicated system control application, an optimum solution may be patching thing

70、s together with a personal computer and fuzzy logic 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

71、 Life On Earth。If intelligent life has appeared anywhere 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 lo

72、gic is an important moment in the development of an intelligent civilization. On earth, we have just arrived at that important moment. You need to know and begin using fuzzy logic.   </p><p>　　The discu

73、ssion so far does not adequately prepare us for reading and understanding most books and articles about fuzzy logic, because of the terminology used by sophisticated authors. Following are explanations of some terms whic

74、h should help in this regard. This terminology was initially established by Dr. Zadeh when he originated the fuzzy logic concept.   </p><p>　　Fuzzy - The degree of fuzziness of a system analysis rule ca

75、n vary between being very precise, in which case we 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

76、 the degree of precision of a system analysis rule. 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

77、 measured by a pressure t</p><p>　　Principle of Incompatibility (previously stated; repeated here) – </p><p>　　As the complexity of a system increases, it becomes more difficult and eventually i

78、mpossible to make a precise statement about its behavior, 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

79、- A fuzzy set is almost any condition for which we have words: short men, tall women, hot, cold, new buildings, accelerator setting, ripe bananas, high intelligence, speed, weight, spongy, etc., where the condition can b

80、e given a value between 0 and 1. Example: A woman is 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 indefin

81、itely rating a great number of things between 0 a</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 hav

82、e a degree of membership in "too fast of" .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 "n

83、o change needed" to determine feedback action 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 ma

84、sses of data, but humans can handle it. The ability 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 character

85、istic is the big difference between humans and digital computers. Emulating this human ability is the challenge facing those who would create computer based artificial intelligence. It is proving very, very difficult to

86、program a computer to have human-li</p><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 angst

87、roms. These words are fuzzy variables.</p><p>　　If, for example, speed of a system is the attrribute being evaluated by fuzzy, "fuzzy" rules, then "speed" is a fuzzy variable. </p>

88、<p>　　Linguistic Variable - Linguistic means 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

89、 variables are: somewhat fast speed, very high speed, real slow speed, excessively high accelerator setting, accelerator setting about right, etc. A fuzzy variable becomes a linguistic variable when we modify it with des

90、criptive words, such as somewhat fast, very high, real 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>　　Univers

91、e of Discourse - Let us make women the object of our 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

92、Universe of Discourse. Universe of Discourse then, 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

93、 solution. A Universe of Discourse is made up of fuzz</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 th

94、e speed of a small steam engine the student had built. 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 a

95、s well as they wanted. Engine speed would either overshoot 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 p

96、roposed by Dr. Lotfi Zadeh, head of the electrical engineering department at the University of California at Berkeley, in the United States. Dr. Zadeh is the originator of the designation "fuzzy", which everyon

97、e suspects was selected to throw a little "pie in the face" of his more orthodox engineering colleagues, some of whom strongly opposed the fuzzy logic concept under any name.</p><p>　　Professor Mam

98、dani and the student, S. Assilian, decided to give 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 th

99、e history books by harnessing the power of a force 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 th

100、ey had done with any other method.  The steam 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 impor

101、tant moment in the history of scientific development. 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, en