ICSC fuzzy logic and Applications. Part of the International ICSC Congress on Computation Intelligence Methods and Applications (CIMA 2001). Bangor, Wales, UK; 1922 June 2001. http://www.icsc-naiso.org/conferences/cima2001/fla2001/
Xfuzzy Home Page fuzzy logic DESIGN TOOLS. It can be also executed in MSWindows by using the environment Cygwin. fuzzy logic E-Book (en español). http://www.imse.cnm.es/Xfuzzy/
Extractions: FUZZY LOGIC DESIGN TOOLS The fuzzy system development environment Xfuzzy integrates a set of tools that ease the user to cover the several stages involved in the design process of fuzzy logic-based inference systems, from their initial description to their final implementation. The sections of this page are linked with the several versions of the environment, with our related scientific publications, and with some didactic material. This new version of Xfuzzy is based on a new specification language (XFL3) which extends the advantages of its predecessor, allowing the use of linguistic hedges as well as new fuzzy operators defined freely by the user. New CAD tools have been included to ease the edition of operator sets and hierarchical systems, to generate 2- and 3-dimmensional graphic outputs, and to monitor the inference process. The tool that applies supervised learning has been quitely renewed so as to include new algorithms as well as pre- and post-processing techniques to simplify the obtained rule bases. Xfuzzy 3.0 has been enterely programmed in Java. Hence, it can be executed on any platform with JRE (Java Runtime Environment) installed. The version 2.1 of Xfuzzy, based on the specification language XFL, includes several CAD tools to describe, verify and synthesize (into software or hardware) fuzzy systems. This version can be compiled and executed in Unix-like operating systems with the X Window system. It can be also executed in MS-Windows by using the environment
School Of Computing Research areas 3D Imaging; Artificial Intelligence; Robotics; fuzzy logic and Medical Imaging. http://www.cse.dmu.ac.uk/computerscience/
Norstrilia A review of this novel, by Bard Bloom, at the fuzzy logic fanzine. http://cornwuff.kittyfox.net/fuzzylogic/fl6/reviews/norstrilia.html
Fuzzy Logic And Sets fuzzy logic Tutorial. Logica Fuzzy (Spanish). Copyright 2000 2004 firstname.lastname@example.org All Rights Reserved. Welcome to our tutorial on fuzzy logic and Sets. http://www.answermath.com/fuzzy_logic_sets.htm
Extractions: All Rights Reserved Welcome to our tutorial on Fuzzy logic and Sets. This theory lets us handle and process information in a similar way as the human brain does. We communicate and coordinate actions with data like you are too young to do that How much does too refer to, whats young? With fuzzy sets we may define sub-sets in a fashion that any element may be part of them in different degrees. With fuzzy rules its possible to compute relationships between fuzzy variables and produce fuzzy outputs. And guess what from these fuzzy output values; we may build boolean and continuous quantities, like a switch status or an amount of money.
Improving Computer Control Of Batch Dyeing Operations Presentation of novel control algorithms, and specific simulation and experimental results for fuzzy logic and adaptive control systems in textile dye houses. Authors Brent Smith and Jun Lu. PDF document. http://www.p2pays.org/ref/03/02348.pdf
Extractions: Click on an image to access the original plate When the movie 2001: A Space Odyssey was released in 1968, the notion of a computer that could recognize and react to human emotion seemed far-fetched and frightening. Filmgoers got a cold chill watching HAL, the homicidal supercomputer on board the spaceship Dis- covery, invent a malfunction in the ship's communications link with Earth, engineer the deaths of four astronauts, and desperately try to talk the sole survivor out of dismantling its memory bank. "Look, Dave," HAL implores, "I can see you're really upset about this. I honestly think you ought to sit down calmly, take a stress pill, and think things over." Some 30 years later, emotive computers are more a promising reality than a sci-fi nightmare. Researchers at MIT, IBM, Sony, and other laboratories are developing technologies that may one day make it possible for your personal computer to sense whether you are happy or sad, anxious or relaxed, interested or boredand to use that information to shape how it interacts with you.
Drugasar Ltd Comprehensive range of heaters including basic gas heater needing no electrical power to electronic models utilising fuzzy logic. http://www.drugasar.co.uk
FANG Fuzzy Logic Page FANGroup fuzzy logic Resources. FAQ fuzzy logic and Fuzzy Expert Systems FAQ fuzzy logic and Softcomputing A brief course in fuzzy logic and Fuzzy Control. http://www.ie.ncsu.edu/fangroup/fuzzy.dir/indexfuzzy.html
Extractions: FAQ: Fuzzy Logic and Fuzzy Expert Systems FAQ: Fuzzy Logic and Soft-computing A brief course in Fuzzy Logic and Fuzzy Control Fuzzy Logic Archive Fuzzy Logic and Neuro-fuzzy resources Yahoo: Fuzzy Logic Fuzzy Sets and Systems by Robert Fuller Research Labs Center for Fuzzy Logic and Intelligent Systems Research Commercial Sites hyperFuzzy Hyperlogic Corp. Fuzzy Systems Engineering Stuff Fuzzy Shower FuzzyCLIPS Books, Journals and Papers Fuzzy Sets and Systems Fuzzy Optimization and Decision Making Newsgroups Newsgroup: comp.ai.fuzzy Mailing Lists Technical University of Vienna Fuzzy Logic Mailing List. Send a mail with the following text in the body:
KnowledgeScape Adaptive Optimization And Expert Control Advanced process control software integrates fuzzy logic, neural networks, genetic algorithms, statistical process control techniques and client server architecture for solutions to the process control industry. http://www.kscape.com/
Extractions: KnowledgeScape advanced process control software integrates fuzzy logic, neural networks, genetic algorithms, statistical process control techniques and client server architecture to provide an easy to use, on line, real time, global optimization solution to the process control industry. Browse our site to learn more about our Company , gather information on our various find someone near you offering a KnowledgeScape solution, or view our interactive demo. AI Learning Section
Fuzzy Logic fuzzy logic. Abstract. The paper gives examples of the fuzzy logic applications, with emphasis on the field of artificial intelligence. fuzzy logic. http://www-pub.cise.ufl.edu/~ddd/cap6635/Fall-97/Short-papers/24.htm
Extractions: This paper gives a general overview of fuzzy logic theory. It describes the concepts of fuzzy sets and operations used in their manipulation, developed by Lofti Zadeh in 1965. The paper gives examples of the fuzzy logic applications, with emphasis on the field of artificial intelligence. "When Theseus returned from slaying the Minotaur, says Plutarch, the Athenians preserved his ship, and as planks rotted, replaced them with new ones. When the first plank was replaced, everyone agreed it was still the same ship. Adding a second plank made no difference either. At some point, the Athenians may have replaced every plank in the ship. Was it a different ship? At what point did it become one?"  The classical logic relies on something being either True or False. A True element is usually assigned a value of 1, while False has a value of 0. Thus, something either completely belongs to a set or it is completely excluded from it. The fuzzy logic broadens this definition of membership. The basis of the logic are fuzzy sets. Unlike in "crisp" sets, where membership is full or none, an object is allowed to belong only partly to one set. The membership of an object to a particular set is described by a real value from the range between and 1. Thus, for instance, an element can have a membership value 0.5, which describes a 50% membership in a given set. Such logic allows a much easier application of many problems that cannot be easily implemented using classical approach.
Center For Applied Control Mission is to promote and conduct research in a broad spectrum of fields including acoustics, aeroacoustics, aeroelasticity, structural dynamics, nonlinear systems, pattern recognition, seismic structural vibration, fuzzy logic active control applications, active noise control, and active magnetic bearings. http://edisto.egr.duke.edu/~hpgavin/CAC/
Extractions: Select Search All Bartleby.com All Reference Columbia Encyclopedia World History Encyclopedia Cultural Literacy World Factbook Columbia Gazetteer American Heritage Coll. Dictionary Roget's Thesauri Roget's II: Thesaurus Roget's Int'l Thesaurus Quotations Bartlett's Quotations Columbia Quotations Simpson's Quotations Respectfully Quoted English Usage Modern Usage American English Fowler's King's English Strunk's Style Mencken's Language Cambridge History The King James Bible Oxford Shakespeare Gray's Anatomy Farmer's Cookbook Post's Etiquette Bulfinch's Mythology Frazer's Golden Bough All Verse Anthologies Dickinson, E. Eliot, T.S. Frost, R. Hopkins, G.M. Keats, J. Lawrence, D.H. Masters, E.L. Sandburg, C. Sassoon, S. Whitman, W. Wordsworth, W. Yeats, W.B. All Nonfiction Harvard Classics American Essays Einstein's Relativity Grant, U.S. Roosevelt, T. Wells's History Presidential Inaugurals All Fiction Shelf of Fiction Ghost Stories Short Stories Shaw, G.B. Stein, G. Stevenson, R.L. Wells, H.G. Reference Columbia Encyclopedia PREVIOUS NEXT ... BIBLIOGRAPHIC RECORD The Columbia Encyclopedia, Sixth Edition. fuzzy logic a multivalued (as opposed to binary) logic developed to deal with imprecise or vague data. Classical logic holds that everything can be expressed in binary terms: or 1, black or white, yes or no; in terms of
MSOE Fuzzy Logic Laboratory Welcome to MSOE s fuzzy logic Laboratory. funded by the NSF ILI program this laboratory is dedicated to teaching fuzzy logic to undergraduates. http://www.msoe.edu/~welch/fuzzy.html
Extractions: So far as the laws of mathematics refer to reality, they are not certain. And so far as they are certain, they do not refer to reality. - Albert Einstein About Fuzzy Logic About the Laboratory Other Fuzzy Sites About Fuzzy Logic: Initially developed by Lotfi Zadeh in 1965, Fuzzy Logic is a paradigm for easily dealing with uncertainty and ambiguity often found in engineering and decision systems. Unlike traditional computing approaches that require a rigid classification of objects and decisions into one category or another (the classic if-then-else approach) fuzzy logic allows for a partial classification of an object into any given class. This simple extension to boolean thinking makes it much easier to model the human thought process and to deal with systems where a clear mathematical model is not known. Consider, for example, a simple home heating system. The traditional system uses a simple thermostat to turn on the furnace when the temperature drops below a certain setpoint and back off when it exceeds another. In this situation the temperature is never held constant and the furnace is continually asked to either run at full or not at all. This can be both uncomfortable and inefficient. By modeling this furnace system using fuzzy or uncertain classifications like cool warm , and hot for temperature and stable slow , and fast for changes in temperature it is much easier to develop a non-linear mapping of current temperature and temperature trend to furnace output. Not only is this often easier to develop than a traditional PID controller it generally results in a more desirable and more efficient operation.
Intelligent Machines And Systems Lab @ San Diego State University Engaged in research and teaching in broad areas of machine intelligence, including applications to robots and systems of such techniques as fuzzy logic, neural networks, genetic algorithms, evolutionary and soft computing, rough sets, and data mining. http://www-rohan.sdsu.edu/~tarokh/lab/
Extractions: Recently, several sponsored research projects have been conducted in the laboratory. Among them are: Intelligent navigation and control of Mars rovers sponsored by NASA Genetic path planning of rovers sponsored by Lockheed-Martin Evaluation of computing performance of Compaq Alpha machines for robotic simulation visualization sponsored by Compaq Robotic person-following sponsored by the CSU Research, Scholarship, and Creative Activity program
Extractions: Fuzzy Logic In Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies, Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables, Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes, Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the
> MATLAB > Fuzzy Logic Mathtools.net The technical computing portal for your scientific and engineering needs - MATLAB fuzzy logic section. http://www.mathtools.net/MATLAB/Fuzzy_Logic/
Extractions: Mathtools.net MATLAB Fuzzy Logic Add Link ... Java MathWorks Fuzzy Logic Toolbox - The Fuzzy Logic Toolbox features a simple point-and-click interface that guides you effortlessly through the steps of fuzzy design, from setup to diagnosis. It provides built-in support for the latest fuzzy logic methods, such as fuzzy clustering and adaptive neuro-fuzzy learning. The Toolbox's interactive graphics let you instantly visualize and fine tune system behavior. DANIELA Neuro-Fuzzy Controller - DANIELA is a Neuro-Fuzzy system for control applications. The system is based on a custom neural device that can implement either Multi-Layer Perceptrons, Radial Basis Functions or Fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy paradigms with finite state automatas and is used to control a walking hexapod. FISMAT: Fuzzy Inference System toolbox - FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, implication relations, and different method of approximate reasoning such as Compositional Rule of Inference (CRI) and Approximate Analogical Reasoning Scheme based on Similarity Measure. Fuzzy Identification Toolbox - The Fuzzy Modeling and Identification toolbox is a collection of Matlab functions for the construction of TakagiSugeno (TS) fuzzy models from data.
> Java > Fuzzy Logic Mathtools.net The technical computing portal for your scientific and engineering needs - Java fuzzy logic section. http://www.mathtools.net/Java/Fuzzy_Logic/
Extractions: Mathtools.net Java Fuzzy Logic Add Link ... Other "Fit" The Fuzzy Bit - A fuzzy Logic demo, including documentation and source code. Cognitive Systems and Artificial Neural Networks at Battelle - Battelle's state-of-the-art fuzzy logic software tools provide an avenue for Battelle researchers and their clients to use fuzzy logic to solve problems. Our team of experts is ready to assist you in reducing your time to market for new products. Fuzzy Analyzer - The Fuzzt Analyzer takes a curve from uncertain or unreliable data,calculates the variations acceptable within a Fuzzy System, and replots the curve more wisely. Fuzzy Controller for an Inverted Pendulum - This is a demonstration of a one-stage Inverted Pendulum controlled by a Fuzzy Java Controller generated by FIDE, a fuzzy design tool by Aptronix, Inc. The blue box underneath is a roller cart controlled by a motor that pulls it to the left or right according to the controller's command. Fuzzy Logic Control Demonstration in Java - Based on Fuzzy theory, first developed by Prof. Zadeh, fuzzy logic control has proved its performances, availability and usefulnesses in many of sophisticated domestic applicances. The following in this web page is an example of infamous 'Cart-Pole' broom balancing, using Fuzzy Logic Control, implemented in Java language.
Extractions: The contents of this note appear as Chapter G6 ``Autonomous Robot Navigation'' in the Handbook of Fuzzy Computation , E. Ruspini, P. Bonissone and W. Pedrycz, Eds. (Oxford Univ. Press and IOP Press, 1998). Abstract The development of techniques for autonomous navigation constitutes one of the major trends in the current research on mobile robotics. In this case study, we discuss how fuzzy computation techniques have be used in the SRI International mobile robot Flakey to address some of the difficult issues posed by autonomous navigation: (i) how to design basic behaviors; (ii) how to coordinate behaviors to execute full navigation plans; and (iii) how to use approximate map information. Our techniques have been validated in both in-house experiments and public events. The use of fuzzy logic has resulted in smooth motion control, robust performance in face of errors in the prior knowledge and in the sensor data, and principled integration between different layers of control. Contents This paper in PostScript format gzip compressed (92 Kbytes) uncompressed (364 Kbytes).