Fuzzy Logic

Fuzzy Logic

A Practical Approach

1st Edition - September 12, 1994

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  • Authors: F. Martin McNeill, Ellen Thro
  • eBook ISBN: 9781483266220

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Description

Fuzzy Logic: A Practical Approach focuses on the processes and approaches involved in fuzzy logic, including fuzzy sets, numbers, and decisions. The book first elaborates on fuzzy numbers and logic, fuzzy systems on the job, and Fuzzy Knowledge Builder. Discussions focus on formatting the knowledge base for an inference engine, personnel detection system, using a knowledge base in an inference engine, fuzzy business systems, industrial fuzzy systems, fuzzy sets and numbers, and quantifying word-based rules. The text then elaborates on designing a fuzzy decision and Fuzzy Thought Amplifier for complex situations. Topics include origins of cognitive maps, Fuzzy Thought Amplifier, training a map to predict the future, introducing the Fuzzy Decision Maker, and merging interests. The publication takes a look at fuzzy associative memory, fuzzy sets as hypercube points, and disk files and descriptions, including Fuzzy Thought Amplifier, Fuzzy Decision Maker, and composing and creating a memory. The text is a valuable source of data for researchers interested in fuzzy logic.

Table of Contents


  • Foreword

    Chapter 1. The Fuzzy World

    Apples, Oranges, Or In Between?

    Is There Life Beyond Math?

    Vague Is Better

    Discovering Fuzziness

    The Uses Of Fuzzy Logic

    Fuzzy Control Systems

    Other Commercial Fuzzy Systems

    The Value Of Fuzzy Systems

    Advantages and Disadvantages

    Fuzzy Decision-Making

    Fuzziness And Asian Nations

    Fuzzy Systems And Uncertainty

    Probability and Bayesian Methods

    Nonprobabilistic Methods

    Fuzzy Systems And Neural Networks

    Chapter 2. Fuzzy Numbers And Logic

    Fuzzy Numbers

    Meet FuzNum Calc

    Performing Fuzzy Arithmetic

    Behind the Scenes With FuzNum Calc

    Fuzzy Sets

    Set Theory

    Touring UniCalc

    Multielement Sets

    Union, Intersection, and Implication

    Difference

    Complement

    Crisp And Fuzzy Logic

    Rules of Inference

    Logical Statements

    As-Then And As-Do Rules—A Sneak Preview

    Quantifying Word-Based Rules

    Chapter 3. Fuzzy Systems on the Job

    Fuzzy Tools

    Fuzzy Knowledge Builder™ for a Fuzzy Expert System

    Fuzzy Decision-Maker™

    Fuzzy Thought Amplifier™

    Fuzzy Systems

    Creating A Fuzzy Control System

    Identify and Name Fuzzy Inputs

    Identify and Name Fuzzy Output

    Create the Fuzzy Membership Functions

    Construct the Rule Base

    Decide How to Execute the Actions

    Fuzzy Business Systems

    Industrial Fuzzy Systems

    Fuzzy-Neuro Sewage Pumping Station

    Fuzzy Insulin Infusion System For Diabetics

    Fuzzy Consumer Products

    Chapter 4. Fuzzy Knowledge Builder™

    Knowledge Builder's Design

    Program Organization

    Program File Structure

    Lunar Lander

    Lunar Lander's Vertical Axis

    Lunar Lander's Horizontal Axis

    Printing Your Graphics Displays

    Personnel Detection System

    Naming and Defining the Dimensions and Sets

    Improving the Matrix's Operation

    Formatting The Knowledge Base For An Inference Engine

    Using A Knowledge Base In An Inference Engine

    Chapter 5. Designing a Fuzzy Decision

    The Decision Process

    Introducing The Fuzzy Decision Maker™

    Deciding Which College To Attend

    Naming Your Goals

    Name Your Constraints

    Name Your Alternatives

    Rank the Importances of Your Goals and Constraints

    How Well Do the Alternatives Satisfy the Goals?

    Regional Transportation System

    Goals

    Constraints

    Alternatives

    Importances

    Satisfactions

    The Decision Process

    Merging Interests

    The Scenario

    The Alternatives

    The Goals

    The Constraints

    George's Version

    Martha's Version

    Comparing the Two Versions

    Inside The Fuzzy Decision Maker

    Importances

    Satisfactions

    The Decision

    Chapter 6. Fuzzy Thought Amplifier™ for Complex Situations

    Dynamic Complexities In Everyday Life

    Origins Of Cognitive Maps

    Crisp Cognitive Maps

    Fuzzy Cognitive Maps

    Fuzzy Thought Amplifier™

    Normal Operation

    "Trained" Operation

    Simple Fuzzy Thought Amplifiers™

    Stable Map

    Oscillation

    Chaos

    Catplant

    Naming and Defining the States

    Creating Events

    Event Values and Names

    Adding Dynamic Graphics

    Running Cycles

    Adding Bias

    Running Cycles with the Added Bias

    Adding Additional States

    Running the Augmented CatPlant

    Health Care System

    The States

    The Events

    Running the Healthcare Map Cycles

    Importance of the Healthcare Map

    Training A Map To Predict The Future

    The Scenario

    The States

    The Events

    Training the Map

    Predicting the Future

    How The Fuzzy Thought Amplifier™ Works

    Definition Method

    Incremental Method

    Training Function

    Concluding Thoughts

    Appendix A. Fuzzy Associative Memory (FAM)

    FAMCALC

    Composing A Memory

    Creating A Memory

    How FamCalc Works

    Step 1

    Step 2

    Appendix B. Fuzzy Sets as Hypercube Points

    Sets As Points

    Using Koskocalc

    Interaction Of A Set And Its Complement

    Far Crisp And Near Crisp

    Measuring A Set's Size

    Interaction Of Two Fuzzy Sets

    Distance

    Subsethood

    Appendix C. Disk Files and Descriptions

    Library Files

    Dr. Fuzzy's Calculators

    Fuzzy Knowledge Builder™ Files

    Example Knowledge Base

    Example Inference Engines

    Example Problems

    Fuzzy Decision Maker™

    Choosing a College

    Legal Problem

    Unemployment

    Financial Planning

    Changing Residence

    Fuzzy Thought Amplifier™

    Readme File

    Appendix D. Inference Engine Programs

    QuickBasic Simple Inference Engine

    QuickBasic Fast Inference Engine

    C Language Inference Engine

    Fuzz-C Inference Engine

    Motorola 68HC05 Assembly Simple Inference Engine

    Appendix E. Other Fuzzy Architecture

    Flops

    How FLOPS Works

    Badger—An Animal Guessing Game

    Parallel FLOPS

    State Machines

    Crisp State Machine

    Fuzzy State Machine

    Putting a Fuzzy State Machine Into Operation

    The Rules and the Inference Method

    Bibliography

    Articles

    Books

    Conference Proceedings

    Index

Product details

  • No. of pages: 312
  • Language: English
  • Copyright: © Academic Press 1994
  • Published: September 12, 1994
  • Imprint: Academic Press
  • eBook ISBN: 9781483266220

About the Authors

F. Martin McNeill

Ellen Thro

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