模糊控制理論-畢業(yè)設(shè)計(jì)外文文獻(xiàn)翻譯-資料下載頁
2024-12-03 00:30本頁面
【導(dǎo)讀】概述模糊邏輯廣泛適用于機(jī)械控制。這個詞本身激發(fā)一個一定的懷疑,試。格性的方法,而這樣的事實(shí),即邏輯涉及能處理的概念,不能被表達(dá)為“對”或“否”,而是因?yàn)椤安糠终鎸?shí)”。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(diǎn)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(yàn),可用于設(shè)計(jì)的控制器。這讓它更容易完成機(jī)械化已成功由人執(zhí)。他闡述了他的觀點(diǎn)在1973年的一篇論文的概念,介紹了語。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。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。,他于1985年的模擬,證明了模糊控制系統(tǒng)對仙臺鐵路的控制。該系統(tǒng)在兩種情況下,保持穩(wěn)定。Yamakawa最終繼續(xù)組織自己。相機(jī)的模糊控制系統(tǒng)采用12輸入,6個輸入了解解現(xiàn)行清晰所提。仿真結(jié)果表明,模糊控制系統(tǒng)可大大降低燃料消耗。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)。數(shù),稱為“模糊集”。
【正文】 rules to finally generate the crisp posite output. Obviously, the greater the truth value of cold, the higher the truth value of high, though this does not necessarily mean that the output itself will be set to high since this is only one rule among many. In some cases, the membership functions can be modified by hedges that are equivalent to adjectives. Common hedges include about, near, close to, approximately, very, slightly, too, extremely, and somewhat. These operations may have precise definitions, though the definitions can vary considerably between different implementations. Very, for one example, squares membership functions。 since the membership values are always less than 1, this narrows the membership function. Extremely cubes the values to give greater narrowing, while somewhat broadens the function by taking the square rootIn practice, the fuzzy rule sets usually have several antecedents that are bined using fuzzy operators, such as AND, OR, and NOT, though again the definitions tend to vary: AND, in one popular definition, simply uses the minimum weight of all the antecedents, while OR uses the imum value. There is also a NOT operator that subtracts a membership function from 1 to give the plementary functionThere are several ways to define the result of a rule, but one of the most mon and simplest is the min inference method, in which the output membership function is given the truth value generated by the premiseRules can be solved in parallel in hardware, or sequentially in software. The results of all the rules that have fired are defuzzified to a crisp value by one of several methods. There are dozens in theory, each with various advantages and drawbacksThe centroid method is very popular, in which the center of mass of the result provides the crisp value. Another approach is the height method, which takes the value of the biggest contributor. The centroid method favors the rule with the output of greatest area, while the height method obviously favors the rule with the greatest output valueThe diagram below demonstrates min inferring and centroid defuzzification for a system with input variables x, y, and z and an output variable n. Note that mu is standard fuzzylogic nomenclature for truth value: Fuzzy control system design is based on empirical methods, basically a methodical approach to trialanderror. The general process is as follows: the system39。s operational specifications and inputs and the fuzzy sets for the the rule the defuzzification through test suite to validate system, adjust details as required6plete document and release to production. Logical interpretation of fuzzy control In spite of the appearance there are several difficulties to give a rigorous logical interpretation of the IFTHEN rules. As an example, interpret a rule as IF temperature is cold THEN heater is high by the first order formula Coldx→ Highy and assume that r is an input such that Coldr is false. Then the formula Coldr→ Hight is true for any t and therefore any t gives a correct control given r. Obviously, if we consider systems of rules in which the class antecedent define a partition such a paradoxical phenomenon does not arise. In any case it is sometimes unsatisfactory to consider two variables x and y in a rule without some kind of functional dependence. A rigorous logical justification of fuzzy control is given in H225。jek39。s book ,where fuzzy control is represented as a theory of H225。jek39。s basic logic. Also in Gerla 2021 a logical approach to fuzzy control is proposed based on the following idea. Denote
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