101 lines
5.6 KiB
Plaintext
101 lines
5.6 KiB
Plaintext
Computer Design
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April 1992
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FUZZY LOGIC IS ANYTHING BUT FUZZY
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- INTERVIEW WITH PROFESSOR LOTFI ZADEH -
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CD: Today fuzzy logic appears to be most widely used in control
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applications, but still seems to be having trouble gaining
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acceptance. How do you view the situation?
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Zadeh: We have to realize that it's very natural for people,
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including myself, to be skeptical when they're presented with
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something that claims to provide a different way of looking at
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things. In 1965 my expectation was that most applications would
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be in the realm of ``humanistic systems,'' such as linguistics,
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social sciences and biological sciences where hard mathematics
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doesn't seem very effective. But then we began to see that
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fuzzy logic could be used in control. In control it is said
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that people want rigor and respectability. But then there are
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many realistic problems that cannot be rigorously defined. Fuzzy
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algorithms for control policy will gain increasing though
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perhaps grudging acceptance because conventional nonfuzzy
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algorithms cannot in general cope with the complexity and ill-
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defined nature of large scale systems. Control theory must
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become less preoccupied with mathematical rigor and precision
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and more concerned with the development of qualitative or
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approximate solutions to pressing real world problems.
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CD: What do you tell people who express doubts about the
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reliability and stability of fuzzy systems?
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Zadeh: In the case of control systems, we do have a theory of
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stability. And presumably that theory can tell you that a
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certain kind of system will be stable. But actually that is
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much less significant from a practical point of view than one
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might think. Once you read the fine print, you find that what
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the theory can tell you is much more limited. It can tell you
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that if you linearize and if you do all sorts of things under
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certain assumptions. The trouble is it's very difficult to say
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whether those assumptions hold or not. So you're left with
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something that is not really comforting. You can't really sleep
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safely if someone using classical theory tells you that some
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control system is stable. Fuzzy systems are course systems.
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Fuzzy control is course control that exploits the tolerance for
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imprecision. So if there is some imprecision and if the
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imprecision can be tolerated, you try to take advantage of it by
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making the system more robust and less susceptible to
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deviation. But still it is correct to say that at this point we
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don't have a theory for stability of fuzzy logic control that is
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nearly as well developed as for classical systems. Stability
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theory is really effective when it comes to linear systems and
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fuzzy systems deal with nonlinearity.
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In the case of fuzzy control, the systems are very complex. In
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many cases you cannot describe really what they do so it is
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difficult to prove or disprove stability. It's not that people
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are stupid, it's that the problems are more complex and it's
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more difficult to come out with some kind of unqualified
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statement. So people compensate for that with simulation. They
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perform many, many trial runs. In the case of the subway in the
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city of Sendai, Japan, I think there were some 300,000
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simulations and 2,000 actual runs to prove the system because
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you do not play with a subway system. So I think the fact that
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the Sendai subway system has functioned perfectly since July 15,
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1987 is a stronger testimony than theory. So here is a system
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where the issues of stability and reliability are of paramount
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importance and it has proved to be successful.
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CD: Is the choice then between devoting a lot of time to
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establishing a mathematical model for classical control in
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advance, or, in fuzzy logic, designing the system and then
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proving and refining it in simulation?
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Zadeh: I think you put it well. The test of any theory is the
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ability to predict. So if you cannot predict what will happen,
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you don't have much of a theory. Many so-called theories flunk
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this test, particularly in economics. In fuzzy systems, instead
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of performing some sort of analysis on paper or on computer that
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will predict how the system will behave, you simulate. So
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simulation is an alternative to prediction. It is not as
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desirable, but in the final analysis it may be more reliable.
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There's always a possibility that your theoretical analysis
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didn't take into consideration certain things. Software is a
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good example. In the final analysis you have to run the
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program. Only actual use will tell you if there are bugs in the
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program or not.
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------------------------------------------------------------
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This is article is provided with permission from Computer
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Design. For subscription information to Computer Design, call
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Paul Westervelt at (913) 835-3161. Do not redistribute in
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any form (written or electronic) without permission from
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Computer Design.
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This information is provided by
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Aptronix FuzzyNet
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408-428-1883 Data USR V.32bis
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