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 --------------------------------------------------------------------- 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 ------------------------------------------------------------------ 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. ----------------------------------------------------------------------- 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". ------------------------------------------------------------------- 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. ----------------------------------------------------------------------- 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 --------------------------------------------------------------- 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. --