Fuzzy Expert Systems and Fuzzy Reasoning / Edition 1

Hardcover (Print)
Buy New
Buy New from BN.com
Used and New from Other Sellers
Used and New from Other Sellers
from $64.00
Usually ships in 1-2 business days
(Save 59%)
Other sellers (Hardcover)
  • All (6) from $64.00   
  • New (4) from $117.47   
  • Used (2) from $64.00   


Coverage is accessible to practitioners and academic readers alike. - Features end-of-chapter problems with answers provided in an appendix. - Includes discussions of rule-based systems not available in any other book. - Includes problem sets and tutorial programs available on the Wiley ftp site.

Read More Show Less

Product Details

  • ISBN-13: 9780471388593
  • Publisher: Wiley
  • Publication date: 12/13/2004
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 424
  • Product dimensions: 6.28 (w) x 9.37 (h) x 0.98 (d)

Meet the Author

WILLIAM SILER, of Southern Dynamic Systems, Inc., is a former academician, having served as chairman of the Biomedical Computer Science program at SUNY Downstate Medical Center, and as professor and chairman of the Biomathematics Department at the University of Alabama at Birmingham. For the past forty years, he has been developing scientific software tools for general use. He is now Senior Scientist at the Kemp-Carraway Heart Institute at Birmingham, Alabama.

JAMES J. BUCKLEY is Associate Professor of Mathematics at the University of Alabama in Birmingham. A prominent researcher in fuzzy mathematics, he has published over 200 papers and several books on the topic.

Read More Show Less

Table of Contents


1 Introduction.

1.1 Characteristics of Expert Systems.

1.2 Neural Nets.

1.3 Symbolic Reasoning.

1.4 Developing a Rule-Based Expert System.

1.5 Fuzzy Rule-Based Systems.

1.6 Problems in Learning How to Construct Fuzzy Expert Systems.

1.7 Tools for Learning How to Construct Fuzzy Expert Systems.

1.8 Auxiliary Reading.

1.9 Summary.

1.10 Questions.

2 Rule-Based Systems: Overview.

2.1 Expert Knowledge: Rules and Data.

2.2 Rule Antecedent and Consequent.

2.3 Data-Driven Systems.

2.4 Run and Command Modes.

2.5 Forward and Backward Chaining.

2.6 Program Modularization and Blackboard Systems.

2.7 Handling Uncertainties in an Expert System.

2.8 Summary.

2.9 Questions.

3 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: I.

3.1 Classical Logic.

3.2 Elementary Fuzzy Logic and Fuzzy Propositions.

3.3 Fuzzy Sets.

3.4 Fuzzy Relations.

3.5 Truth Value of Fuzzy Propositions.

3.6 Fuzzification and Defuzzification.

3.7 Questions.

4 Fuzzy Logic, Fuzzy Sets, and Fuzzy Numbers: II.

4.1 Introduction.

4.2 Algebra of Fuzzy Sets.

4.3 Approximate Reasoning.

4.4 Hedges.

4.5 Fuzzy Arithmetic.

4.6 Comparisons between Fuzzy Numbers.

4.7 Fuzzy Propositions.

4.8 Questions.

5 Combining Uncertainties.

5.1 Generalizing AND and OR Operators.

5.2 Combining Single Truth Values.

5.3 Combining Fuzzy Numbers and Membership Functions.

5.4 Bayesian Methods.

5.5 The Dempster–Shafer Method.

5.6 Summary.

5.7 Questions.

6 Inference in an Expert System I.

6.1 Overview.

6.2 Types of Fuzzy Inference.

6.3 Nature of Inference in a Fuzzy Expert System.

6.4 Modification and Assignment of Truth Values.

6.5 Approximate Reasoning.

6.6 Tests of Procedures to Obtain the Truth Value of a Consequent from the Truth Value of Its Antecedent.

6.7 Summary.

6.8 Questions.

7 Inference in a Fuzzy Expert System II: Modification of Data and Truth Values.

7.1 Modification of Existing Data by Rule Consequent Instructions.

7.2 Modification of Numeric Discrete Fuzzy Sets: Linguistic Variables and Linguistic Terms.

7.3 Selection of Reasoning Type and Grade-of-Membership Initialization.

7.4 Fuzzification and Defuzzification.

7.5 Non-numeric Discrete Fuzzy Sets.

7.6 Discrete Fuzzy Sets: Fuzziness, Ambiguity, and Contradiction.

7.7 Invalidation of Data: Non-monotonic Reasoning.

7.8 Modification of Values of Data.

7.9 Modeling the Entire Rule Space.

7.10 Reducing the Number of Classification Rules Required in the Conventional Intersection Rule Configuration.

7.11 Summary.

7.12 Questions.

8 Resolving Contradictions: Possibility and Necessity.

8.1 Definition of Possibility and Necessity.

8.2 Possibility and Necessity Suitable for MultiStep Rule-Based Fuzzy Reasoning.

8.3 Modification of Truth Values During a Fuzzy Reasoning Process.

8.4 Formulation of Rules for Possibility and Necessity.

8.5 Resolving Contradictions Using Possibility in a Necessity-Based System.

8.6 Summary.

8.7 Questions.

9 Expert System Shells and the Integrated Development Environment (IDE).

9.1 Overview.

9.2 Help Files.

9.3 Program Editing.

9.4 Running the Program.

9.5 Features of General-Purpose Fuzzy Expert Systems.

9.6 Program Debugging.

9.7 Summary.

9.8 Questions.

10 Simple Example Programs.

10.1 Simple FLOPS Programs.

10.2 Numbers.fps.

10.3 Sum.fps.

10.4 Sum.par.

10.5 Comparison of Serial and Parallel FLOPS.

10.6 Membership Functions, Fuzzification and Defuzzification.

10.7 Summary.

10.8 Questions.

11 Running and Debugging Fuzzy Expert Systems I: Parallel Programs.

11.1 Overview.

11.2 Debugging Tools.

11.3 Debugging Short Simple Programs.

11.4 Isolating the Bug: System Modularization.

11.5 The Debug Run.

11.6 Interrupting the Program for Debug Checks.

11.7 Locating Program Defects with Debug Commands.

11.8 Summary.

11.9 Questions.

12 Running and Debugging Expert Systems II: Sequential Rule-Firing.

12.1 Data Acquisition: From a User Versus Automatically Acquired.

12.2 Ways of Solving a Tree-Search Problem.

12.3 Expert Knowledge in Rules; auto1.fps.

12.4 Expert Knowledge in a Database: auto2.fps.

12.5 Other Applications of Sequential Rule Firing.

12.5.1 Missionaries and Cannibals.

12.6 Rules that Make Themselves Refireable: Runaway Programs and Recursion.

12.7 Summary.

12.8 Questions.

13 Solving “What?” Problems when the Answer is Expressed in Words.

13.1 General Methods.

13.2 Iris.par: What Species Is It?

13.3 Echocardiogram Pattern Recognition.

13.4 Schizo.par.

13.5 Discussion.

13.6 Questions.

14 Programs that Can Learn from Experience.

14.1 General Methods.

14.2 Pavlov1.par: Learning by Adding Rules.

14.3 Pavlov2.par: Learning by Adding Facts to Long-Term Memory.

14.4 Defining New Data Elements and New: RULEGEN.FPS.

14.5 Most General Way of Creating New Rules and Data Descriptors.

14.6 Discussion.

14.7 Questions.

15 Running On-Line in Real-Time.

15.1 Overview of On-Line Real-Time Work.

15.2 Input/Output On-Line in Real-Time.

15.3 On-Line Real-Time Processing.

15.4 Types of Rules Useful in Real-Time On-Line Work.

15.5 Memory Management.

15.6 Development of On-Line Real-Time Programs.

15.7 Speeding Up a Program.

15.8 Debugging Real-Time Online Programs.

15.9 Discussion.

15.10 Questions.





Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star


4 Star


3 Star


2 Star


1 Star


Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation


  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)