【正文】 停車 ,當(dāng)線于 1987 年開(kāi)業(yè)。 1988年 ,日本建立了國(guó)際模糊工程實(shí)驗(yàn)室 ,建立合作安排 48 公司進(jìn)行模糊控制的研究。 另外一個(gè)例子是 ,三菱工業(yè) 空調(diào)設(shè)計(jì)采用 25 加熱規(guī)則和 25 冷卻規(guī)則。在 1995 年美泰克公司推出的一個(gè)“聰明” 基于模糊控制器洗碗機(jī) ,“一站式感應(yīng)模塊”包括熱敏電阻器 ,用來(lái)溫度測(cè)量 。 賦予了“映射輸入變量的隸屬函數(shù)和進(jìn)入真理價(jià)值 ,單片機(jī)然后做出決定為 采取何種行動(dòng)基于一套“規(guī)則” ,每一組的形式。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。地圖傳感器輸入級(jí)或其他輸入 ,比如開(kāi)關(guān)等等 ,到合適的隸屬函數(shù)和真理的價(jià)值。這個(gè)結(jié)果是用來(lái)與其他規(guī)則的結(jié)果 ,最終產(chǎn)生脆復(fù)合輸出。 在實(shí)踐中 ,模糊規(guī)則集 ,通常有幾 個(gè)來(lái)路綜合利用模糊運(yùn)算 ,如 ,或者 ,不 ,雖然再次定義每每變化 ,在一個(gè)受歡迎的定義 ,只是利用最小重量的雛形 ,而或采用最大值。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(gè)困難出現(xiàn)給一個(gè)嚴(yán)謹(jǐn)?shù)倪壿嫿忉?If Then規(guī)則。然后給 出一個(gè)可能的輸出的 ,我們把為真理程度的表示。 a conductivity sensor, to measure detergent level from the ions present in the wash。s basic logic. Also in Gerla 2021 a logical approach to fuzzy control is proposed based on the following idea. Denote 。 and a magostrictive sensor to read spin rate. The system determines the optimum wash cycle for any load to obtain the best results with the least amount of energy, detergent, and water Research and development is also continuing on fuzzy applications in software, as opposed to firmware, design, including fuzzy expert systems and integration of fuzzy logic with neuralwork and socalled adaptive geic software systems, with the ultimate goal of building selflearning fuzzy control systems. Fuzzy sets The input variables in a fuzzy control system are in general mapped into by sets of membership functions similar to this, known as fuzzy sets. The process of converting a crisp input value to a fuzzy value is called fuzzification. A control system may also have various types of switch, or ONOFF, inputs along with its analog inputs, and such switch inputs of course will always have a truth value equal to either 1 or 0, but the scheme can deal with them as simplified fuzzy functions that happen to be either one value or anotherGiven mappings of input variables into membership functions and truth values, the microcontroller then makes decisions for what action to take based on a set of rules, each of the formIn one example, the two input variables are brake temperature and speed that have values defined as fuzzy sets. The output variable, brake pressure, is also defined by a fuzzy set that can have values like static, slightly increased, slightly decreased, and so on. This rule by itself is very puzzling since it looks like it could be used without bothering with fuzzy logic, but remember that the decision is based on a set of rules: All the rules that apply are invoked, using the membership functions and truth values obtained from the inputs, to determine the result of the ruleThis result in turn will be mapped into a membership function and truth value controlling the output variableThese results are bined to give a specific crisp answer, the actual brake pressure, a procedure known as defuzzification. This bination of fuzzy operations and rulebased inference describes a fuzzy expert systemTraditional control systems are based on mathematical models in which the control system is described using one or more differential equations that define the system response to its inputs. Such systems are often implemented as PID controllers proportionalintegralderivative controllers. They are the products of decades of development and theoretical analysis, and are highly effectiveIf PID and other traditional control systems are so welldeveloped, why bother with fuzzy control? It has some advantages. In many cases, the mathematical model of the control process may not exist, or may be too expensive in terms of puter processing power and memory, and a system based on empirical rules may be more effe