背景 历史Fuzzy logic was first proposed by Lotfi A Zadeh of the University of California at Berkeley in a 1965 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 Other research followed， with the first industrial application， a cement kiln built in Denmark， coming on line in Fuzzy systems were largely ignored in the US because they were associated with artificial intelligence， a field that periodically oversells itself， especially in the mid-1980s， resulting in a lack of credibility within the commercial The Japanese did not have this 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 Their ideas were adopted， and fuzzy systems were used to control accelerating， braking， and stopping when the line opened in Another event in 1987 helped promote interest in fuzzy 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" 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 Observers 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 The system maintained stability in both Yamakawa eventually went on to organize his own fuzzy-systems research lab to help exploit his patents in the Following such demonstrations， Japanese engineers developed a wide range of fuzzy systems for both industrial and consumer In 1988 Japan established the Laboratory for International Fuzzy Engineering (LIFE)， a cooperative arrangement between 48 companies to pursue fuzzy Japanese consumer goods often incorporate fuzzy Matsushita vacuum cleaners use microcontrollers running fuzzy algorithms to interrogate dust sensors and adjust suction power Hitachi washing machines use fuzzy controllers to load-weight， fabric-mix， and dirt sensors and automatically set the wash cycle for the best use of power， water， and As a more specific example， Canon developed an autofocusing camera that uses a charge-coupled device (CCD) to measure the clarity of the image in six regions of its field of view and use the information provided to determine if the image is in It also tracks the rate of change of lens movement during focusing， and controls its speed to prevent The camera's fuzzy control system uses 12 inputs: 6 to obtain the current clarity data provided by the CCD and 6 to measure the rate of change of lens The output is the position of the The fuzzy control system uses 13 rules and requires 1 kilobytes of As another example of a practical system， an industrial air conditioner designed by Mitsubishi uses 25 heating rules and 25 cooling A temperature sensor provides input， with control outputs fed to an inverter， a compressor valve， and a fan Compared to the previous design， the fuzzy controller heats and cools five times faster， reduces power consumption by 24%， increases temperature stability by a factor of two， and uses fewer The enthusiasm of the Japanese for fuzzy logic is reflected in the wide range of other applications they have investigated or implemented: character and handwriting recognition; optical fuzzy systems; robots， including one for making Japanese flower arrangements; voice-controlled robot helicopters， this being no mean feat， as hovering is a "balancing act" rather similar to the inverted pendulum problem; control of flow of powders in film manufacture; elevator systems; and so Work on fuzzy systems is also proceeding in the US and Europe， though not with the same enthusiasm shown in J The US Environmental Protection Agency has investigated fuzzy control for energy-efficient motors， and NASA has studied fuzzy control for automated space docking: simulations show that a fuzzy control system can greatly reduce fuel Firms such as Boeing， General Motors， Allen-Bradley， Chrysler， Eaton， and Whirlpool have worked on fuzzy logic for use in low-power refrigerators， improved automotive transmissions， and energy-efficient electric In 1995 Maytag introduced an "intelligent" dishwasher based on a fuzzy controller and a "one-stop sensing module" that combines a thermistor， for temperature measurement; a conductivity sensor， to measure detergent level from the ions present in the wash; a turbidity sensor that measures scattered and transmitted light to measure the soiling of the wash; and a magnetostrictive sensor to read spin The system determines the optimum wash cycle for any load to obtain the best results with the least amount of energy， detergent， and It even adjusts for dried-on foods by tracking the last time the door was opened， and estimates the number of dishes by the number of times the door was 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 neural-network and so-called adaptive "genetic" software systems， with the ultimate goal of building "self-learning" fuzzy control systems
