================================================================= FUZZY SYSTEMS TOOLBOX --- FAQ FILE September 7, 1994 ================================================================= What if I have trouble running the demonstration programs? ---------------------------------------------------------- If your problem seems to be related to the process of trying to download the files from this ftp site, the simplest alternative is to call ITP Education Division Customer Service at 1-800-354-9706 and request the "Fuzzy Systems Toolbox Demo Package" (ISBN 0-534-94593-7). If you encounter other problems, check your hardware and software configuration, especially with regard to MATLAB specific settings. What is the Fuzzy Systems Toolbox? ---------------------------------- The Fuzzy Systems Toolbox is a library of MATLAB(R) functions for the design and simulation of systems defined with fuzzy logic. Also included is a user's guide (including both tutorial and reference material) and example applications. What is Fuzzy Logic? -------------------- In traditional logic a statement is either true of false. An object is either a member of a set or it is not. We often represent such truth values with the numbers 0 and 1. In fuzzy logic statements can be partially true and objects can be partial members of a set. Fuzzy truth values are represented with numbers in the interval [0.0, 1.0]. For example, the truth of the statement "Today is hot" might be 1.0 if the temperature is 92 degrees. This statement would have a truth of 0.0 if it is freezing (32 degrees). If the temperature is 62 degrees then the truth of the statement might be 0.5. It would be a half truth. Traditional logic operators have been generalized to apply to fuzzy logic values. For example, the fuzzy complement is often defined as follows: NOT(A) = 1 - A Thus, if "Today is hot" has a truth value of 0.7, then the truth of the statement "Today is not hot" has a truth value of 0.3. In this way fuzzy logic forms a mathematical foundation for the logic of partial truths. This makes it possible for machines to model qualitative concepts, such as "hot" and "cold" as readily as quantitative concepts such as temperature. Why is fuzzy logic useful? -------------------------- Fuzzy logic allows problems to be solved using concepts that come naturally to human beings. This yields several advantages: 1) Fuzzy systems are easy to design. 2) Fuzzy systems are easy to modify, whether to improve performance, add new capabilities, or respond to changes in their environment. 3) Fuzzy logic allows human problem solving to be automated. This helps in problems where a human expert is available, but a rigorous mathematical solution is unobtainable or impractical. 4) Fuzzy systems produce good solutions to subjective problems defined by humans. Examples include business decisions, consumer decisions, etc. Fuzzy systems also tend to be mathematically simple yielding two more advantages. 5) Fuzzy systems are easy to analyze. 6) Fuzzy systems are very computationally efficient. What applications is the Fuzzy Systems Toolbox suitable for? ------------------------------------------------------------ Fuzzy logic has been usefully applied in many fields. Here are a few of the areas where the toolbox is applicable: - Business decision making - Control systems - Medical diagnosis - Pattern recognition - Signal Processing - Time series prediction Here is a partial list of industries where fuzzy logic has been successfully applied: Aerospace, automotive, business, chemical engineering, defense, electronics, financial markets, manufacturing, medicine, mining, robotics, signal processing, telecommunications, and transportation. What kinds of fuzzy systems can be represented? ----------------------------------------------- The toolbox allows fuzzy systems to be defined with M-files containing arbitrary fuzzy logic statements, or with matrices representing sets of fuzzy logic rules. What size of fuzzy systems can be designed and simulated? --------------------------------------------------------- The toolbox supports fuzzy systems with any number of inputs, output, and rules. Does the toolbox come with any applications? -------------------------------------------- The toolbox comes with the following example applications: Business decisions: - Pricing a product - Selecting a marketing company - Choosing an automobile Control systems: - Truck backer-upper - Inverted pendulum on a cart Pattern recognition: - Character recognition - Data clustering Other: - Function approximation Does the Toolbox support SIMULINK(TM)? -------------------------------------- Yes. The toolbox provides a fuzzy system block to allow fuzzy systems to be simulated in SIMULINK. The inverted pendulum application is implemented both within MATLAB and within SIMULINK. How easy is it to learn to use the Fuzzy Systems Toolbox? --------------------------------------------------------- Very easy. The user's guide provides a detailed introduction to fuzzy logic and its application using toolbox functions. The applications (mentioned above) were chosen to show the ways the toolbox can be used. Here is the table of contents of the user's guide: 1. Introduction 2. Fuzzy Fundamentals 3. Fuzzy Rules 4. Decision-Making Systems 5. Control Systems 6. Pattern Recognition Systems 7. Advanced Topics 8. Reference Who should buy the Fuzzy Systems Toolbox? ----------------------------------------- Engineers, medical practitioners, business people, and anyone who makes complex decisions or solves complex problems. Or, as the authors are fond of saying: "Everyone! Nobody should be without it." Who are these authors? ---------------------- The Fuzzy Systems Toolbox was written by Mark Beale and Howard Demuth, also also authors of The MathWorks' best-selling Neural Network Toolbox. ================================================================= MATLAB is a registered trademark, SIMULINK is a trademark of The MathWorks, Inc.