【正文】 語言變量” ,在這篇文章中相當(dāng)于一個變量定義為一個模糊集合。這個詞本身激發(fā)一個一定的懷疑 ,試探相當(dāng)于“倉促的邏輯”或“虛假的邏輯” ,但“模糊”不是指一個部分缺乏嚴(yán)格性的方法 ,而這樣的事實 ,即邏輯涉及能處理的概念 ,不能被表達(dá)為“對”或“否” ,而是因為“部分真實”。雖然遺傳算法和神經(jīng)網(wǎng)絡(luò)可以執(zhí)行一樣模糊邏輯在很多情況下 ,模糊邏輯的優(yōu)點是解決這個問題的方法 ,能夠被鑄造方面接線員能了解 ,以 便他們的經(jīng)驗 ,可用于設(shè)計的控制器。其他研究打亂了 ,第二次工業(yè)應(yīng)用中 ,水泥窯建在丹麥 ,即將到來的在線 1975。他們的想法是被接受了 ,并將模糊系統(tǒng)用來控制加速、制動、和停車 ,當(dāng)線于 1987 年開業(yè)。 這次展示給觀察者家們留下了深刻的印象 ,以及后來的實驗 ,他登上一 Yamakawa 酒杯包含水或甚至一只活老鼠的頂部的鐘擺。 1988年 ,日本建立了國際模糊工程實驗室 ,建立合作安排 48 公司進(jìn)行模糊控制的研究。它也可以追蹤變化的速率在鏡頭運動的重點 ,以及它的速度以防止控制超調(diào)。 另外一個例子是 ,三菱工業(yè) 空調(diào)設(shè)計采用 25 加熱規(guī)則和 25 冷卻規(guī)則。 模糊系統(tǒng)的相關(guān)研究工作也在美國和歐洲進(jìn)行著。在 1995 年美泰克公司推出的一個“聰明” 基于模糊控制器洗碗機 ,“一站式感應(yīng)模塊”包括熱敏電阻器 ,用來溫度測量 。 研究和開發(fā)還繼續(xù)模糊應(yīng)用軟件 ,作為反對固件設(shè) 計 ,包括模糊專家系統(tǒng)模糊邏輯與整合神經(jīng)網(wǎng)絡(luò)和所謂的自適應(yīng)遺傳軟件系統(tǒng) ,其最終目的是建立“自主學(xué)習(xí)”模糊控制系統(tǒng)。 賦予了“映射輸入變量的隸屬函數(shù)和進(jìn)入真理價值 ,單片機然后做出決定為 采取何種行動基于一套“規(guī)則” ,每一組的形式。 所有的規(guī)則都調(diào)用申請 ,使用模糊隸屬度函數(shù)和誠實得到輸入值 ,確定結(jié)果的規(guī)則。這類系統(tǒng)通常被作為“ PID 控制器”他們是產(chǎn)品的數(shù)十年的發(fā)展建設(shè)和理論分析 ,是非常有效的。這種系統(tǒng)可以很容易地通過增加新的規(guī)則升級來提高性能或添加新功能。地圖傳感器輸入級或其他輸入 ,比如開關(guān)等等 ,到合適的隸屬函數(shù)和真理的價值。從三人至七人通常是適當(dāng)?shù)母采w曲線所需要的范圍的一個輸入值 ,或“宇宙的話語“在模糊術(shù)語。這個結(jié)果是用來與其他規(guī)則的結(jié)果 ,最終產(chǎn)生脆復(fù)合輸出。這些操作可能有明確的定義 ,雖然可能有很大差別的定義不同的實現(xiàn)。 在實踐中 ,模糊規(guī)則集 ,通常有幾 個來路綜合利用模糊運算 ,如 ,或者 ,不 ,雖然再次定義每每變化 ,在一個受歡迎的定義 ,只是利用最小重量的雛形 ,而或采用最大值。順序結(jié)果所有的規(guī)則 ,其中的幾個方法。方法更利于統(tǒng)治質(zhì)心與輸出最大的區(qū)域 ,而高程法顯然更利于規(guī)則和最大的輸出值。 。 邏輯解釋模糊控制 盡管有幾個困難出現(xiàn)給一個嚴(yán)謹(jǐn)?shù)倪壿嫿忉?If Then規(guī)則。在任何情況下它有時是不考慮兩個變量和在一條規(guī)則沒有某種功能的依賴。然后給 出一個可能的輸出的 ,我們把為真理程度的表示。 Fuzzy Control From Wikipedia20 November 2021 Overview Fuzzy logic is widely used in machine control. The term itself inspires a certain skepticism, sounding equivalent to halfbaked logic or bogus logic, but the fuzzy part does not refer to a lack of rigour in the method, rather to the fact that the logic involved can deal with concepts that cannot be expressed as true or false but rather as partially true. Although geic algorithms and neural works can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humansHistory and applications Fuzzy logic was first proposed by Lotfi A. Zadeh of the University of California at Berkeley in a 1965 paper. He elaborated on his ideas in a 1973 paper that introduced the concept of linguistic variables, which in this article equates to a variable defined as a fuzzy set. Other research followed, with the first industrial application, a cement kiln built in Denmark, ing on line in 1975Fuzzy systems were largely ignored in the . because they were associated with artificial intelligence, a field that periodically oversells itself, especially in the mid1980s, resulting in a lack of credibility within the mercial domainThe Japanese did not have this prejudice. Interest in fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the superiority of fuzzy control systems for the Sendai railway. Their ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when the line opened in 1987Another event in 1987 helped promote interest in fuzzy systems. During an international meeting of fuzzy researchers in Tokyo that year, Takeshi Yamakawa demonstrated the use of fuzzy control, through a set of simple dedicated fuzzy logic chips, in an inverted pendulum experiment. This is a classic control problem, in which a vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forthObservers were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine g