【正文】 ld 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 result in turn will be mapped into a membership function and truth value controlling the output 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 system“.Traditional 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 systems are often implemented as ”PID controllers“(proportionalintegralderivative controllers).They are the products of decades of development and theoretical analysis, and are highly PID and other traditional control systems are so welldeveloped, why bother with fuzzy control? It has some 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 , fuzzy logic is well suited to lowcost implementations based on cheap sensors, lowresolution analogtodigital converters, and 4bit or 8bit onechip microcontroller systems can be easily upgraded by adding new rules to improve performance or add new many cases, fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the current control （論文）外文文獻翻譯Fuzzy control in detailFuzzy controllers are very simple consist of an input stage, a processing stage, and an output input stage maps sensor or other inputs, such as switches, thumbwheels, and so on, to the appropriate membership functions and truth processing stage invokes each appropriate rule and generates a result for each, then bines the results of the , the output stage converts the bined result back into a specific control output most mon shape of membership functions is triangular, although trapezoidal and bell curves are also used, but the shape is generally less important than the number of curves and their three to seven curves are generally appropriate to cover the required range of an input value, or the ”universe of discourse“ in fuzzy discussed earlier, the processing stage is based on a collection of logic rules in the form of IFTHEN statements, where the IF part is called the ”antecedent“ and the THEN part is called the ”consequent“.This rule uses the truth value of the ”temperature“ input, which is some truth value of ”cold“, to generate a result in the fuzzy set for the ”heater“ output, which is some value of ”high“.This result is used with the results of other rules to finally generate the crisp posite , 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 some cases, the membership functions can be modified by ”hedges“ that are equivalent to 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。a conductivity sensor, to measure detergent level from the ions present in the wash。optical fuzzy systems。黃石理工學(xué)院畢業(yè)設(shè)計（論文）外文文獻翻譯Fuzzy Control From Wikipedia November 2011OverviewFuzzy logic is widely used in machine 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 genetic algorithms and neural networks 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 makes it easier to mechanize tasks that are already successfully performed by and applicationsFuzzy logic was first proposed by Lotfi of the University of California at Berkeley in a 1965 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 research followed, with the first industrial application, a cement kiln built in Denmark, ing on line in systems were largely ignored in the 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 Japanese did not have this 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 ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when the line opened in event in 1987 helped promote interest in fuzzy 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“ 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 were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine glass containing water or even a live mouse to the top of the system maintained stability in both eventually went on to organize his own fuzzysystems research lab to help exploit his patent