545 lines
27 KiB
Plaintext
545 lines
27 KiB
Plaintext
comp.ai.fuzzy #129 (36 more) [1]
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From: farzin@apollo3.ntt.jp (Farzin Mokhtarian)
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[1] Complete contents of the booklet "Clearly Fuzzy"
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Originator: sehari@vincent1.iastate.edu
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Organization: Iowa State University of Science and Technology, Ames, Iowa.
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Date: Thu Jan 21 15:01:34 MET 1993
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Lines: 959
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--MORE--(1%)
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Complete contents of the booklet "Clearly Fuzzy" by:
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OMRON Corporation
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International Public Relations Section
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3-4-10, Toranomon, Minato-ku
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Tokyo, 105 Japan
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Tel: 81-3-3436-7139
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Fax: 81-3-3436-7029
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Contact: Tadashi Katsuno
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---------------------------------------------------------------------
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1. Introduction
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Fuzzy Logic is attracting a great deal of attention in the industrial
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world and among the general public today. Quick to recognize this
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revolutionary control concept, OMRON seriously began to study Fuzzy
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theory and technology in 1984, back when the term "Fuzzy" was still
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relatively unknown.
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Just three years later, OMRON stunned the academic world and triggered
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today's boom when it exhibited its first super-high-speed Fuzzy
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controller. It was developed jointly with Assistant Professor Takeshi
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Yamakawa of Kumamoto University and shown at the Second International
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Conference of the International Fuzzy Systems Association (IFSA).
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OMRON has since dedicated itself to exploring the potential of this
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innovative technology. The company invited Professor Lotfi A. Zadeh,
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the founder of Fuzzy theory, to be a senior advisor, and welcomed
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researchers from China, a country known as one of the leaders in
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Fuzzy Logic study. As a result of technological exchanges with
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research institutes of various countries, OMRON's Fuzzy Logic-related
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activities are reaching a global scale. Since 1984, OMRON has applied
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for a total of 700 patents, making the company an international leader
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in Fuzzy Logic technology.
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OMRON's enthusiasm for Fuzzy Logic stems from the company's goal of
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creating harmony between people and machinery. As a key technology
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in OMRON's future, we will be working hard to strengthen and refine
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this exciting technology and give it truly useful applications at
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production sites, in offices, in public facilities, as well as in
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everyday life.
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We hope this booklet will be useful in increasing your knowledge,
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or at least in sparking your interest in this exciting technology.
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OMRON Corporation
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------------------------------------------------------------------
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2. Truly Friendly Machines
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2.1. Arrival of the Fuzzy Boom
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The current Fuzzy boom was triggered by the presentation of trial
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Fuzzy applications at the Academic Conference of the International
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Fuzzy Systems Association (IFSA). The obvious feasibility of these
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forerunners of today's Fuzzy Logic deeply impressed conference
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attendees. Nowadays in Japan, Fuzzy Logic is successfully being
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applied to industrial systems such as elevators and subways and
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to an array of consumer electronic products. Convenient Fuzzy Logic
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home electrical appliances include washing machines that sense the
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dirtiness and type of fabric to automatically determine water flow
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and detergent requirements; and vacuum cleaners capable of detecting
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not only the presence but the degree of dust on a floor!
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2.2. Shades of Gray
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The theory of Fuzzy Logic was introduced to the world by Professor
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Lotfi A. Zadeh of the University of California at Berkeley.
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Professor Zadeh observed that conventional computer logic is
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incapable of manipulating data representing subjective or vague
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human ideas, such as "an attractive person" or "pretty hot".
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Computer logic previously envisioned reality only in such simple
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terms, as on or off, yes or no, and black or white. Fuzzy Logic
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was designed to allow computers to determine valid distinctions
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among data with shades of gray, working similarly in essence to
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the processes which occur in human reasoning. Accordingly, Fuzzy
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technologies are designed to incorporate Fuzzy theories into
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modern control and data processing, to create more user-friendly
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systems and products.
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2.3. A Warm Welcome in the Orient
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Since Fuzzy Logic's world debut 26 years ago, theoretical and
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practical studies have been carried out in countries around
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the globe; Fuzzy Logic research is currently underway in over
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30 nations including the USA, Europe, Japan and China. It may
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be surprising to some to note that the world's largest number
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of Fuzzy Logic researchers are in China, with over 10,000
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scientists and technicians presently hard at work. Japan ranks
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second in Fuzzy Logic manpower, followed by Europe and the USA.
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Among all nations however, Japan is currently positioned at the
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leading edge of Fuzzy Logic application studies. So it may be
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that the popularity of Fuzzy Logic in the Orient reflects the
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fact that Oriental thinking more easily accepts the concept of
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"Fuzziness".
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2.4. Fuzzy - Part of Every Day at OMRON
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OMRON is also hard at work in the Fuzzy Logic field. Projects
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currently on the go at OMRON include working to establish a
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Fuzzy technological base, developing new products incorporating
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Fuzzy theory, adapting Fuzzy Logic technology to existing
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products and conducting seminars for interested audiences
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from outside OMRON. Fuzzy Logic has in fact grown to such
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proportions that it has become an integral part of the new
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corporate culture at OMRON.
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3. "Fuzzy" Made Clear
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3.1. What is "Fuzzy"?
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Originally stemming from the fuzz which covers baby chicks, the term
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"fuzzy" in English means "indistinct, blurred, not sharply delineated
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or focused." This term is "flou" in French and pronounced "aimai" in
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Japanese. In the academic and technological worlds, "Fuzzy" is a
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technical term. Fuzziness in this sense represents ambiguity or
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vagueness based on human intuitions rather than being based on
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probability. Twenty six years ago, Professor Lotfi A. Zadeh
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introduced "Fuzzy sets" to adapt the concepts of fuzzy boundaries to
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science. Fuzzy theory was devised around the Fuzzy sets and a new
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field of engineering known as "Fuzzy Engineering" was born. Although
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"Fuzzy sets" may sound very mathematical, the baept with fuzzy boundaries which can not be handled by
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conventional computers using the binary system. This is where
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Fuzzy theory comes in. Let's suppose that we have concluded that
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middle age is 45. However, people 35 or 55 years of age can not
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be said to be "definitely not middle-aged". There is a feeling,
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however, that the implication of "middle age" is somewhat
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different inside those boundaries. On the contrary, those younger
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than 30 or older than 60 can be considered "definitely not
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middle-aged". Such a concept can be represented by a characteristic
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function called the "membership function" having a grade between 0
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and 1. A Fuzzy set is represented by this membership function.
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However, note that the grade within the membership function can be
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e age as soon as their
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next birthday arrives! This sort of unnaturalness is due to
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inflexible value assignments. Such concepts with distinct values
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of 0 or 1 are called "crisp sets" as opposed to the "Fuzzy sets".
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4. Fuzzy Theory in Action
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4.1. Fuzzy Algorithm
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One example of Fuzzy theory applications is the handling of
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approximate numbers. If approximately 2 is added to approximately 6,
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the result will be something around 8. People often make this sort
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of calculation. For instance, we frequently estimate the result when
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performing a calc
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computers, which must have crisp data with which to work.
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4.2. The Logic in Fuzzy Logic
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Another field that applies Fuzzy theory concerns artificial
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intelligence, termed "Fuzzy Logic". One of the differences between
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Fuzzy Logic and conventional binary logic is that the truth value
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in Fuzzy Logic can be any value between 0 and 1, while that in
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binary logic is either 0 or 1. Another difference is that the
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Fuzzy proposition includes "fuzzi is a reasoning method using Fuzzy theory, whereby
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human knowledge is expressed using linguistic rules ("If A is B,
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then C is D") with variables B and D. Fuzzy inference is also called
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"daily inference" or "common sense inference" since it is performed
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by ordinary people. However, conventional computers that employ
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binary logic can not handle this reasoning. The use of Fuzzy theory
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enables the development of an expert system that can handFuzzy inference
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is possible even when the meaning of the fact differs slightly
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from the given knowledge. Drawing a conclusion like "Add a little
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cold water", Fuzzy inference matches the conclusion based on human
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experience, intuition, or possibly even reality.
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The "knowledge" part of Fuzzy inference has the structure "if A is
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B, then C is D" (example: "If the water is very hot, add plenty of
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cold water"). Concepts such as "very hot" and "plenty of cold
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water" are subjective and thus represented by Fuzzy sets.
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As you may know, Fuzzy theory was devised for the purpose of
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enabling machines to handle subjective human ideas and operate
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based on advanced knowledge as well as applications of human
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beings' intricate experiences.tomobile and its distance to the automobile in front. Amount of
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control is expressed in terms of Braking strength.
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(1) Express experience and expertise in the form of rules.
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With Fuzzy inference control, these rules are called "production
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rules". They are represented in the form of "If X is A, then Y is B".
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To put it more simply, let's consider two rules as follows:
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tance between the two cars and the car speed (antecedent parts)
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and the level of speed reduction, or braking strength (consequent
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part), are not numeric values but are represented by "Fuzzy Sets"
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expressed through linguistic rules. The distance between the two
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cars and the speed have a multiple number of Fuzzy values and are
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therefore called "Fuzzy variables". Hence, values (lmately 0) labels. Many Fuzzy controllers
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use seven labels, as in the OMRON FZ-3000 Fuzzy Controller, for
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example.
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(3) Replace linguistic production rules with codes for simpler
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expression.
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Although production rules can be expressed with everyday language,
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codes are used to simplify the input to the actual Fuzzy Controllers.: If X1 = M and X2 = L, then Y = M.
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(4) Execute Fuzzy inference control.
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When the rules are programmed into the Fuzzy Controller and it is put
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into operation, the Controller will output the most valid control
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value based on the variable input conditions.
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1) Establish grades (validity) of input in relation to the Fuzzyhe
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smaller value of the grades of inputs. This process is called
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"determining MIN (minimum)".
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Rule 1: As g11 = 0.4 and g12 = 0.2, the grade (MIN value) of
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antecedent part (g1) = 0.2.
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Rule 2: As g21 = 0.7 and g22 = 0.6, the grade (MIN value) of
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antecedent part (g2) = 0.6.
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3) Adjust the membership function of the consequent part.
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e based on each of
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these rules (adjusted Fuzzy Sets of the consequent parts), the final
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conclusion is then determined by summing the Fuzzy Sets of the
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conclusions for each rule. This process is called "determining MAX
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(maximum)".
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This process considers several variable factors, and is thus very
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similar to the human thinking process.
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With Fuzzy Controess.
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Expressing human experience in the form of a mathematical formula
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is very difficult, perhaps impossible. In contrast, Fuzzy inference
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control has the following advantages over conventional control:
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1) Expression of control is easy as it need only derive localized
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control rules for each location (or event) in the control range.
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2) It therefore handles complex input/output by using many contotal number
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of rules.
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o Logical Control
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Fuzzy inference control rules are expressed logically using simple
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linguistic rules ("If A is B, then C is D"). Because everyday
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language can be used, Fuzzy inference control proves ideal for
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expressing the sophisticated knowledge of experts and incorporating
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valuable intuitiony the machine operator or
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others.
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2) The operator can easily interpret the effect or outcome of each
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rule.
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6. Growing Up: Fuzzy Technology Catches On
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6.1. The Birth and Evolution of Fuzzy
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6.2. Is "Fuzziness" Really Better?
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Dr. Zadeh was one of the original founders of the modern control theory
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and remains an authority in this field. Modern control theory is exact,
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precise, and logical, harboring no hint of "fuziness".
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Today, however, the subjects of control have become increasingly larger
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in scale, in turn requiring more advanced and complex control systems,
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like those used to control robots also takes an extremely long time to
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execute the programs. Dr. Zadeh devised Fuzzy theory to overcome
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these debilitating limitations of modern theory.
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There was also another, probably more important factor that encouraged
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him to come up with a new idea. Conventional computers work by
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identifying the factor which seems to have the strongest influence on
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the systems to be controlled, since it is impossible to simultaneously
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command all the factors that affect the system. In other woe, capable of accurate and fast
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computation. However, as the conditional parameters include many
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hypotheses, the computer may sometimes yield a ridiculous conclusion
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contrary to what common sense would lead us to expect. This is caused
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by its attempts to replace "fuzziness" with fixed numeric values.
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Thus, it became necessary to develop a theory capable of dealing
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with the vagueness prevalent in everyday decisions.
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6.3. Strmany criticized him for not
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fulfilling his duty as a scientist.
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6.4. A Profile of Professor Zadeh
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You may want to know a little about the Professor. Here is a
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very brief profile:
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Lotfi A. Zadeh was born in Iran on February 4, 1921. In 1956,
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he was a visiting member of the Institute for Advanced Study in
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Princeton, New Jersey and held numhe IEEE and AAAS. He is also
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a member of the National Academy of Engineering. Now, Dr.
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Zadeh is a senior advisor to OMRON Corporation.
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6.5. A Motivating Debate
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Here is a little story about how Fuzzy Logic was invented. One
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day, Dr. Zadeh got into a long argument with a friend about who
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was more beautiful, his wife or his friend's. Each
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The first applications of Fuzzy theory were primarily industrial,
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such as process control for cement kilns. Then, in 1987, the
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first Fuzzy Logic-controlled subway was opened in Sendai in
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northern Japan. There, Fuzzy Logic controllers make subway
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journeys more comfortable with smooth braking and acceleration.
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In fact, all the driver has to do is push the start button!
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Fuzzy Major Applications
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Automation Steel/iron manufacturing, water purification,
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manufacturing lines and robots, train/elevator
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operation control, consumer products, etc.
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Instrumentation Sensors, measuring instruments, voice/character
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and analysis recognition, et7. Historically Speaking ...
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The year 1990 witnessed the 25th anniversary of the invention of
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Fuzzy theory. It has undergone numerous transformations since its
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inception with a variety of Fuzzy Logic applications emerging in
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many industrial areas. Dividing these past years into different
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stages, the early 1970s are the "theoretical study" stage, the
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period ater
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becoming the Japan Office of the International Fuzzy
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Systems Association (IFSA)).
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1973: Zadeh introduces a methodology for describing systems
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using language that incorporates fuzziness.
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1974: Dr. Mamdani of the University of London, UK succeeds
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with an experimental Fuzzy control for a steam engine.
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1980: F. L. Smidth & Co. A/S, Denmark, implements Fuzzy
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theory in cement kiln control (the world's first
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practical implementation of Fuzzy theory).
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1983A Fuzzy Future
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7.1. Fuzzy Fever Hits Japan
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1987 marked the start of Japan's so-called "Fuzzy boom", reaching
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a peak in 1990. A wide variety of new consumer products since then
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have included the word "Fuzzy" on their labels and have been
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advertised as offering the ultimate in convenience.
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For instance, Fuzzy Logic found its way into the electronic fuel
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injection controls and automatic cruise control ston and the
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rest is taken care of by the machine. It automatically judges
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the material, the volume and the dirtiness of the laundry and
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chooses the optimum cycle and water flow. In air conditioners,
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Fuzzy Logic saves energy because it starts cooling more
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strongly only when a sensor detects people in the room.
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We could go on and on with examples of camcorders, television
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sets, and even fund management systems. The sweeping
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popularity of Fuzzy Logic in Japan might even surprise
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Dr. Zadeh, its founder.
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7.2. No Limits: Promise for the Future
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Just from these few examples, it is clear that Fuzzy Logic
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encompasses an amazing array of applica is described in child care
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books. They may drink a little or a lot depending on their
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physical condition, mood, and other factors. She conceived
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a Fuzzy Logic program that would recommend how much to feed
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the baby. The program determines the appropriate amount of
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milk according to a knowledge base that includes the child's
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personality, physical condition, and some environmenerived from everyday activities in the
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home, like the Fuzzy ventilation system. It uses Fuzzy Logic
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to switch a fan on and off as dictated by its knowledge base
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of the amount of smoke, odors, and room temperature and
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humidity. The Fuzzy bath, for example, has a controller that
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keeps the temperature of the water juvative application of Fuzzy Logic.
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-------------ally advanced company achieved and how? What does
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the future hold for this exciting Fuzzy Logic? Through an
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interview conducted in February 1991 with General Manager
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Masayuki Oyagi of OMRON's Fuzzy Technology Business Promotion
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Center, we hope to answer these questions.
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Q. How did OMRON become involved with Fuzzy Logic technology?
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A. In the early 1980s, we were mportance.
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His encouragement led to the formation of the Fuzzy Project
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team, now the Fuzzy Technology Business Promotion Center,
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which conducts basic studies and explores new business
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opportunities.
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Q. OMRON's R&D efforts have given rise to numerous original
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applications for Fuzzy Logic. Could you give some examples?
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A. The most obvious examd a robot
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which can grasp something "pretty" soft and fragile - tofu
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(bean curd); and a can sorting machine capable of
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identifying cans by color. Overall, OMRON has more than
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100 successful applications, 20 of which are now available
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to the public.
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As 1991 progresses, you can expect more OMRON Fuzzy Logic-based
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products to be introduced. Toorm of Fuzzy Logic.
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Considering the diversity of OMRON's products, this is a
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challenging and significant goal.
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OMRON's R&D investments account for approximately 7% of its
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total sales and I think Fuzzy Logic research represents
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nearly 1%.
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Q. OMRON is not alone in the Fuzzy Logic business. How does it
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distinguish itself from digital and analog units, at
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virtually every speed, inference scale and computation capacity.
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OMRON also offers Fuzzy Logic products in complete sets,
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including chips, software, and development tools, which can be
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used both in-house and by customers. Almost eight years of
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experience with Fuzzy Logic have gone into all of these products.
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There are an afits that
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Fuzzy Logic can offer. Any business operates towards goals,
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such as major performance improvements, cost reductions,
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miniturizing, or others. To attain these goals, businesses
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will usually refine their operations, generally without
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concern for the kind of technology used. But they do care
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about whether the technology can really work for them. Whereessing, computation, memory or output. In other words, it
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can manage "fuzziness". The logic itself is purely mathematical,
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so the results are not "fuzzy" but rather very clear and precise.
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Consider the can sorting machine which I mentioned earlier. With
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Fuzzy Logic, a computer can be instructed to sort cans according
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to their colors such as "addition to developing applications involves many
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people. As an indication, at least 1,000 people have taken a
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Fuzzy Logic seminar.
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Some are members of the Laboratory for International Fuzzy
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Engineering Research (LIFE). One person from our Fuzzy Technology
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Business Promotion Center is now working at OMRON Advanced
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Systems, Inc. i employees and our customers. Although most of these activities
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are within Japan, we plan to expand them to other countries this
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year.
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The first product scheduledely aiming for simultaneous worldwide release. This coming
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spring, a Fuzzy Logic product showroom will open at OMRON
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Electronics, Inc. in Schaumburg, Illinois.
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A. I think there are positive and negative feelings about this term.
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In its early days, "Fuzzy" was not considered an academic term.
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Because of this, however, people got the impression that this
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technology was something quite singular which, I think, gave it
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more impact. On the down side, people thought that its results
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or ability would be "fuzzy", and questioned the product
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reliability.
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nd French and German groups have been
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visiting OMRON regularly since 1989. This makes me confident that
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Fuzzy Logic technology will grow rapidly in both US and Europe in
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the near future.
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If consumer electronics giants such as GE introduce products with
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Fuzzy Logic, you may see a boom even larger than the one
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experienced in Japan lasIntelligence" (AI).
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The left hemisphere of a human brain is used for logical
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processes, like reading and talking, while the right hemisphere
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is for intuitive and emotional mechanisms as well as unconscious
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information processing. Conventional computers imitate the left
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side, while Fuzzy Logic plays the role of the right side.
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In chess, for ntegrating conventional computers with Fuzzy Logic,
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|
expert systems, neural networks, and other technologies.
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OMRON's goal is to create machines that approximate human
|
|
intelligence and capabilities, and yet still be compact and
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inexpensive.
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|
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|
The 1990 Fuzzy Logic boom, I think, was the first wave which
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accurately reflected the direction of the tech.
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|
1987 Assistant Professor Takeshi Yamakawa of Kumamoto University
|
|
(now Professor of Kyushu Institute of Technology) introduces
|
|
super high-speed Fuzzy controller, test-manufactured by
|
|
OMRON, at the 2nd Conference of the International Fuzzy
|
|
Systems Association.
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|
1988 World's first super high-speed Fuzzy controller, FZ-1000,
|
|
marketed.
|
|
OMg Fuzzy Logic technology introduced,
|
|
including chips, controllers, and software.
|
|
Fuzzy Technology Business Promotion Center established.
|
|
Bank note feeding mechanism using Fuzzy Logic developed
|
|
for ATMs.
|
|
Fuzzy hybrid control method developed.
|
|
1990 "LUNA-FuzzyRON" Fuzzy Logic software development support
|
|
system developed.
|
|
Fuzzy Logic human body sensor developed.
|
|
Fuzzy controller related ga
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|
---------------------------------------------------------------
|
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10. Fuzzy Logic Products
|
|
|
|
OMRON has released numerous innovative products that use Fuzzy
|
|
Logic. A few of those products scheduled for release overseas
|
|
are listed below:
|
|
|
|
o FP speed)).
|
|
* Bus interface similar to that of an SRAM allows connection to
|
|
various CPUs.
|
|
* Fuzzy Logic operation can be accomplished on a single chip
|
|
(Single mode).
|
|
* High 12-bit resolution.
|
|
* Up to 128 rules applicable for each inference (Expanded mode).
|
|
|
|
o FS-10AT Fuzzy Software Tool
|
|
BM PC-AT expansion slot.
|
|
* Uses the rules and membership functions created by the FS-10AT.
|
|
* Provided with driver software, allows Fuzzy inference to run
|
|
with the user's software.
|
|
* Applications include evaluation and field tests of the FP-3000,
|
|
and addition of Fuzzy Logic functions to personal computers.
|
|
|
|
o E5AF Fuzzy Temperature Controller
|
|
|
|
The industry's first temperature controller to employ Fuzzy Logic.
|
|
|
|
* Highly precise (+/- 0.3% error) and fast response to external
|
|
disparameter setting. Fuzzy Logic parameters
|
|
can be programmed to fit the application.
|
|
* Ideal for use in physical/chemical equipment, industrial
|
|
furnaces, and semiconductor manufacturing equipment.
|
|
|
|
------------------------------------------------------------------
|
|
|
|
11. Fuzzy Logic Technologies
|
|
|
|
OMRsh dispensers (CDs) are easily affected by
|
|
ambient humidity, conveyance conditions, etc., which in turn makes
|
|
stable bank note feeding difficult. With the aid of Fuzzy Logic,
|
|
this new mechanism keeps the gap between the rollers at the
|
|
optimum level, notably increasing the reliability of ATMs and CDs
|
|
as well as reducing the need for maintenance.
|
|
|
|
--
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