Fuzzy Systems and Machine Intelligence Essay -- Artificial Intelligenc

Fuzzy Systems and Machine Intelligence Abstract: Our natural language is perhaps the most powerful form of communicating information for any given problem or situation. Combining natural language and numerical information into fuzzy systems provides the framework to represent knowledge, constraints and inference procedures. Fuzzy systems provide advantages in the development of systems solutions that perform tasks such as automatic modeling, prediction, pattern recognition, and optimal decision making, control and planning. With this, fuzzy systems are an essential tool for industrial and manufacturing systems engineering. Fuzzy logic is a different approach to representing uncertainty - it emphasizes the double meanings of words in describing events - rather than the uncertainty about whether an event will occur, and allows decision-making under that uncertainty. Fuzzy logic attempts to capture the imperfect way we describe concepts, and works with them to form conclusions. The wonderful thing about fuzzy logic is how you can apply everyday language to a problem. A descriptive sentence such as: "a little noisy, a lot of dirt, and deep carpet", can be decoded by a fuzzy logic system to perform various tasks based on the knowledge derived from the terms. An interesting thing about fuzzy logic is that it is always trying to work, and by tweaking the system, the programmer is simply "showing" the system how to do a better job. The operator is still in control, but the fuzzy logic is mimicking how an operator would react given the same situation. Introduction to the topic: Fuzzy logic is a system analysis and modeling approach that allows an easier transition between complex human thought processes and the ... ...sed to answer specific questions and provide definitions and examples of applications. Addresses are provided to those locations. The Fuzzy Logic Frequently Asked Questions Archive: ht tp://www.uni-passau.de/archive/faq/comp.answers/fuzzy-logic/ Brubaker, David I. "Fuzzy Rules and Membership Functions from Data," Huntington Technical Brief, July 1993, No. 40. Cox, Earl. The Fuzzy Systems Handbook: A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems. Academic Press, New York, 1994. Kosko, Bart. Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs, 1992. Mcneill, Daniel, and Freiberger, Paul, Fuzzy Logic, Simon and Schuster, 1992. Zadeh, Lotfi A. "Fuzzy Sets." Information & Control, Vol. 8, 1965, pp338-353. Zurada, Jacek M. Artificial Neural Systems. West Publishing Co., New York, 1992.