模糊逻辑外文资料翻译(编辑修改稿)

模糊逻辑外文资料翻译(编辑修改稿)内容摘要：

you have to drive down a modern city street was used by our ancestors to successfully organize and carry out chases to drive wooly mammoths into pits, to obtain food, clothing and bone tools. Human beings have the ability to take in and evaluate all sorts of information from the physical world they are in contact with and to mentally analyze, average and summarize all this input data into an optimum course of action. All living things do this, but humans do it more and do it better and have bee the dominant species of the planet. If you think about it, much of the information you take in is not very precisely defined, such as the speed of a vehicle ing up from behind. We call this fuzzy input. However, some of your input is reasonably precise and nonfuzzy such as the speedometer reading. Your processing of all this information is not very precisely definable. We call this fuzzy processing. Fuzzy logic theorists would call it using fuzzy algorithms (algorithm is another word for procedure or program, as in a puter program). Fuzzy logic is the way the human brain works, and we can mimic this in machines so they will perform somewhat like humans (not to be confused with Artificial Intelligence, where the goal is for machines to perform EXACTLY like humans). Fuzzy logic control and analysis systems may be electromechanical in nature, or concerned only with data, for example economic data, in all cases guided by IfThen rules stated in human language.The fuzzy logic analysis and control method is, therefore: 1)Receiving of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control.2)Processing all these inputs according to human based, fuzzy IfThen rules, which can be expressed in plain language words, in bination with traditional nonfuzzy processing.3)Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing defuzzified, crisp value. Measured, nonfuzzy data is the primary input for the fuzzy logic method. Examples: temperature measured by a temperature transducer, motor speed, economic data, financial markets data, etc. It would not be usual in an electromechanical control system or a financial or economic analysis system, but humans with their fuzzy perceptions could also provide input. There could be a human intheloop. In the fuzzy logic literature, you will see the term fuzzy set. A fuzzy set is a group of anything that cannot be precisely defined. Consider the fuzzy set of old houses. How old is an old house? Where is the dividing line between new houses and old houses? Is a fifteen year old house an old house? How about 40 years? What about years? The assessment is in the eyes of the beholder. Other examples of fuzzy sets are: tall women, short men, warm days, high pressure gas, small crowd, medium viscosity, hot shower water, etc. When humans are the basis for an analysis, we must have a way to assign some rational value to intuitive assessments of individual elements of a fuzzy set. We must translate from human fuzziness to numbers that can be used by a puter. We do this by assigning assessment of conditions a value from zero to . For how hot the room is the human might rate it at .2 if the temperature were below freezing, and the human might rate the room at .9, or even , if it is a hot day in summer with the air conditioner off. You can see these perceptions are fuzzy, just intuitive assessments, not precisely measured facts. By making fuzzy evaluations, with zero at the bottom of the scale and at the top, we have a basis for analysis rules for the fuzzy logic method, and we can acplish our analysis or control project. The results seem to turn out well for plex systems or systems where human experience is the only base from which to proceed, certainly better than doing nothing at all, which is where we would be if unwilling to proceed with fuzzy rules.[12] Novices using personal puters and the fuzzy logic method can beat . mathematicians using formulas and conventional programmable logic controllers. Fuzzy logic makes use of human mon sense. This mon sense is either applied from what seems reasonable, for a new system, or from experience, for a system that has previously had a human operator. Here is an example of converting human experience for use in a control system: I read of an attempt to automate a cement manufacturing operation. Cement manufacturing is a lot more difficult than you would think. Through the centuries it has evolved with human feel being absolutely necessary. Engineers were not able to automate with conventional control. Eventually, they translated the human feel into lots and lots of fuzzy logic IfThen rules based on human experience. Reasonable success was thereby obtained in automating the plant. Objects of fuzzy logic analysis and control may include: physical control, such as machine speed, or operating a cement plant。 financial and economic decisions。 psychological conditions。 physiological conditions。 safety conditions。 security conditions。 production improvement and much more. This book will talk about fuzzy logic in control applications controlling machines, physical conditions, processing plants, etc. It should be noted that when Dr. Zadeh invented fuzzy logic, it appears he had in mind applying fuzzy logic in many applications in addition to controlling machines, such as economics, politics, biology, etc. Thank You Wozniak (Apple Computer), Jobs (Apple Computer), Gates (Microsoft) and Ed Roberts (the MITS, Altair entrepreneur) for the Personal Computer. Without personal puters, it would be difficult to use fuzzy logic to control machines and production plants, or do other analyses. Without the speed and versatility of the personal puter,。