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s president and all the directors just sold every share of stock they own, then WE sell! A fuzzy logic system does not have to be directed toward electromechanical systems. The fuzzy logic system could be, for example, to provide buysell decisions to trade 30 million US dollars against the Japanese yen. Fuzzy logic controllers can control solenoids, stepper motors, linear positioners , etc., as well as, or concurrently with, continuous feedback control loops. Where there is continuous feedback control of a control loop, the response for varying degrees of error can be nonlinear, tailoring the response to meet unique or experience determined system requirements, even anomalies. Controllers typically have several inputs and outputs. The handling of various tasks, such as monitoring and manding a control loop and monitoring various inputs, with mands issued as appropriate, would all be sequenced in the puter program. The program would step from one task to the other, the program receiving inputs from and sending mands to the converter/controller. Inputs for a fuzzy logic controlled mechanical/physical system could be derived from any of thousands of real world, physical sensors/transducers. The Thomas Register has over 110 pages of these devices. Inputs for financial trading could e from personal assessments or from an ASCII data munication feed provided by a financial markets quote service. Progress in Fuzzy Logic, From a slow beginning, fuzzy logic grew in applications and importance, until now it is a significant concept worldwide. Intelligent beings on the other side of our galaxy and throughout the universe have probably noted and defined the concept. Personal puter based fuzzy logic control is pretty amazing. It lets novices build control systems that work in places where even the best mathematicians and。s first ever fuzzy logic control system ....... and went directly into the history books by harnessing the power of a force in use by humans for 3 million years, but never before defined and used for the control of machines. The controller worked right away, and worked better than anything they had done with any other method. The steam engine speed control graph using the fuzzy logic controller appeared 。 repeated here) – As the plexity of a system increases, it bees more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of plexity where the fuzzy logic method born in humans is the only way to get at the problem. Fuzzy Sets A fuzzy set is almost any condition for which we have words: short men, tall women, hot, cold, new buildings, accelerator setting, ripe bananas, high intelligence, speed, weight, spongy, etc., where the condition can be given a value between 0 and 1. Example: A woman is 6 feet, 3 inches tall. In my experience, I think she is one of the tallest women I have ever met, so I rate her height at .98. This line of reasoning can go on indefinitely rating a great number of things between 0 and 1. In fuzzy logic method control systems, degree of membership is used in the following way. A measurement of speed, for example, might be found to have a degree of membership in too fast of .6 and a degree of membership in no change needed of .2. The system program would then calculate the center of mass between too fast and no change needed to determine feedback action to send to the input of the control system. This is discussed in more detail in subsequent chapters. Summarizing Information Human processing of information is not based on twovalued, offon, eitheror logic. It is based on fuzzy perceptions, fuzzy truths, fuzzy inferences, etc., all resulting in an averaged, summarized, normalized output, which is given by the human a precise number or decision value which he or she verbalizes, writes down or acts on. It is the goal of fuzzy logic control systems to also do this. The input may be large masses of data, but humans can handle it. The ability to manipulate fuzzy sets and the subsequent summarizing capability to arrive at an output we can act on is one of the greatest assets of the human brain. This characteristic is the big difference between humans and digital puters. Emulating this human ability is the challenge facing those who would create puter based artificial intelligence. It is proving very, very difficult to program a puter to have humanlike intelligence. Fuzzy Variable Words like red, blue, etc., are fuzzy and can have many shades and tints. They are just human opinions, not based on precise measurement in angstroms. These words are fuzzy variables. If, for example, speed of a system is the attrribute being evaluated by fuzzy, fuzzy rules, then speed is a fuzzy variable. Linguistic Variable Linguistic means relating to language, in our case plain language words. Speed is a fuzzy variable. Accelerator setting is a fuzzy variable. Examples of linguistic variables are: somewhat fast speed, very high speed, real slow speed, excessively high accelerator setting, accelerator setting about right, etc. A fuzzy variable bees a linguistic variable when we modify it with descriptive words, such as somewhat fast, very high, real slow, etc. The main function of linguistic variables is to provide a means of working with the plex systems mentioned above as being too plex to handle by conventional mathematics and engineering formulas. Linguistic variables appear in control systems with feedback loop control and can be related to each other with conditional, ifthen statements. Example: If the speed is too fast, then back off on the high accelerator setting. Universe of Discourse Let us make women the object of our consideration. All the women everywhere would be the universe of women. If we choose to discourse about (talk about) w