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