WORLDCOMP'07 Tutorial

WHY DO WE NEED FUZZY LOGIC?
Recent technological advances have made it possible to develop computers that are extremely fast and efficient for numerical computations. However, these computers lack the abilities of humans and animals in processing cognitive information acquired by natural sensors. For example, the human brain routinely performs tasks like recognizing a face in an unfamiliar crowd in 100-200 ms whereas a computer can take days to accomplish a task of lesser complexity. The use of fuzzy logic can emulate the desirable computing aspects found in humans and animals. Engineers and scientists have had many remarkable accomplishments such as putting people on the moon and returning them safely to Earth, sending spacecraft to the far reaches of the solar system, sending rovers to explore the surface of Mars, exploring the oceans depths, designing computers that can perform billions of computations per second, developing the nuclear bomb, mapping the human genome, and constructing a scanning tunneling microscope that can move individual atoms. But alongside many outstanding achievements using unintelligent systems, there have been many abysmal failures that include modeling the behavior of economic, political, social, physical, and biological systems. Engineers have been unable to develop technology that can decipher sloppy handwriting, recognize oral speech as well as a human can, translate between languages as well as a human interpreter, drive a car in heavy traffic as well as a human can, walk with the agility of a human or animal, replace the combat infantry soldier, determine the veracity of a statement by a human subject with an acceptable degree of accuracy, replace judges and juries, summarize a complicated document, and explain poetry or song lyrics. These remaining challenges and many more can benefit from fuzzy logic.

WHAT IS FUZZY LOGIC?
Certainty and precision have much too often become an absolute standard in design, decision making, and control problems. The excess of precision and certainty in engineering and scientific research and development is often providing unrealizable solutions. Fuzzy logic, based on the notion of relative graded membership, can deal with information arising from computational perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, and a lower cost solution in the development of intelligent systems for decision making, identification, recognition, optimization, and control.

WHAT ARE SOME APPLICATIONS OF FUZZY LOGIC?
Fuzzy logic has been used in numerous applications such as data mining, facial pattern recognition, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, intelligent communication networks, knowledge-based systems for multiobjective optimization of power systems, weather forecasting systems, models for new product pricing or project risk assessment, medical diagnosis and treatment plans, and stock trading.

The objective of this tutorial is to provide a clear and rapid introduction to key aspects of fuzzy logic including uncertainty, fuzzy sets, linguistic variables, fuzzy rule bases, computational theory of perceptions, computing with words, and fuzzy math. This tutorial seeks to introduce fuzzy logic concepts mainly through examples of applications in data mining and linguistic evaluations.

TUTORIAL TOPICS:

1. Introduction to Intelligent Systems
2. Certainty and Precision
3. Uncertainty and Imprecision in Perception and Cognition
4. Human Perception and Cognition
5. Fuzzy Logic
6. Computing with Words and Computational Theory of Perceptions
7. Fuzzy Logic in Linguistic Evaluations
8. Fuzzy Logic in Data Mining Applications
9. More on Fuzzy Math (as time permits)

This tutorial is for anybody who wishes to learn the theory and applications of fuzzy logic, and will be extremely useful for many people involved in research and development including computer scientists, engineers (computer, electrical, mechanical, civil, chemical, aerospace, agricultural, biomedical, environmental, geological, industrial, mechatronics), mathematicians, social scientists (economics, management, political science, psychology), natural scientists (biology, chemistry, earth science, physics), business analysts, public policy analysts, jurists, medical researchers, etc.

Ashu M. G. Solo is an electrical and computer engineer, mathematician, writer, and entrepreneur. His primary research interests are in new branches of math, intelligent systems, public policy, and the application of intelligent systems in control systems, computer architecture, power systems, optimization, pattern recognition, decision making, and public policy. Solo has about 50 publications in these and other fields. He co-developed some of the best published methods for maintaining power flow in and multiobjective optimization of radial power distribution system operations. Solo has served on 52 international program committees for 50 research conferences and 2 research multiconferences. He is the principal of Maverick Technologies America Inc. Solo previously served honorably as an infantry officer and platoon commander understudy in the Cdn. Army Reserve.

Ashu M. G. Solo,
Principal/R&D Engineer
Maverick Technologies America Inc.
Suite 808
1220 North Market Street
Wilmington, DE 19801
U.S.A.
Email: amgsolo at mavericktechnologies.us