Computational Intelligence PC Tools

Markus Bonner (bonner@dbai.tuwien.ac.at)
Wed, 26 Jun 1996 11:50:33 +0200


From: Russ Eberhart <eberhart@tech.iupui.edu>
To: fuzzy-owner@vexpert.dbai.tuwien.ac.at
Content-Length: 5537
X-Lines: 128

***** ANNOUNCING A NEW BOOK *****

"Computational Intelligence PC Tools"

by
Russell C. Eberhart, Roy W. Dobbins and Patrick K. Simpson

Copyright 1996 Academic Press Professional
ISBN 0-1222-8630-8
(Being published in July 1996)

Goals and Target Audience

Computational Intelligence PC Tools is a book targeted for senior
undergraduate and first-year graduate students in engineering and
computer science. It is also written for engineers, computer scientists,
and others who want to learn about computational intelligence and its
practical applications. At the end of the first 10 chapters, exercises
are presented that test the understanding of the material presented, and
that give the opportunity to expand knowledge of computational
intelligence tools beyond the chapter contents. Executable code for
major paradigm implementations is provided on a diskette with the book.
Source code (written in C/C++) and additional executable code is
available from the authors. An electronic version of viewgraphs for
professors and instructors teaching a course based upon the book is
available.

Outline

Table of Contents

Acknowledgments

Introduction
Presents the goals and objectives of the text, and describes its
intended audience. Presents an outline of the book, with a brief
description of each part, chapter and appendix. Discusses use of the
text in a course.

Chapter 1: Background
Discusses background of neural networks, fuzzy logic and evolutionary
computation, and how they form the basis for the unified field called
computational intelligence (CI).

Chapter 2: History
Reviews history of CI component technologies, and how they have been
combined to form computational intelligence. The focus is on people,
rather than just on theory or technology.

Chapter 3: Neural Network Theory and Paradigms
Reviews the concepts associated with neural networks. Included are
terminology and symbology, and discussions of the three features of any
neural network: architecture, processing element transfer functions, and
learning algorithms. A number of network paradigms are discussed and
compared. Preprocessing and postprocessing are examined.

Chapter 4: Neural Network Implementations
Presents implementations of back-propagation, learning vector
quantization, and radial basis function networks. (Software is on the
diskette.)

Chapter 5: Evolutionary Computation Theory and Paradigms
Reviews the four basic methodologies of evolutionary computation.
Included are genetic algorithms, evolutionary programming, evolution
strategies and genetic programming.

Chapter 6: Evolutionary Computation Implementations
Includes implementations of a Genesis-like genetic algorithm and a
particle swarm optimizer. (Software is on the diskette.)

Chapter 7: Fuzzy Logic Theory and Paradigms
Discusses fuzzy reasoning, the basics of fuzzy set theory, and fuzzy
membership functions. Presents examples of fuzzy reasoning systems
using both Mamdani and Takagi-Sugeno methods. Examines measures of
fuzziness.

Chapter 8: Fuzzy Logic Implementation
Presents an implementation of a fuzzy expert system. The implementation
has both linear and nonlinear membership functions, and various
fuzzification and defuzzification techniques. (Software is on the
diskette.)

Chapter 9: Computational Intelligence Theory and Concepts
Discusses unified field of computational intelligence. Offers
definitions. Discusses computational intelligence in context with other
intelligence types. Compares adaptation with learning, and
stochasticity with chaos.

Chapter 10: Computational Intelligence Implementations
Presents an implementation of a fuzzy min-max neural network. Also
included is an evolutionary fuzzy expert system. (Software is on the
diskette.)

Chapter 11: Performance Metrics
Examines issues related to measuring how well a CI implementation is
performing. Performance measures discussed include percent correct,
average sum-squared error, normalized error, evolutionary algorithm
effectiveness measures, receiver operating characteristic curves
measurements, confusion matrices, cost functions, and chi-square
goodness-of-fit.

Chapter 12: Analysis and Explanation
Presents analysis and explanation tools that are used to explain how CI
systems do what they do. Included are sensitivity analysis, Hinton
diagrams, and explanation facilities. Application of CI tools to
explanation facilities is reviewed.

Chapter 13: Case Study Summaries
Several case studies are summarized in this chapter. Each summary cites
references where more information can be obtained, and discusses what
makes the case study significant. Included are summaries describing
electroencephalogram (EEG) spike detection, battery state of charge
determination, schedule optimization, and control system design.

Glossary
Presents a comprehensive glossary of terms relevant to neural networks,
evolutionary computation, and fuzzy logic.

References
Contains a unified reference list for the book, including over 230
entries.

Appendices
Appendix A contains a description of software on the diskette and an
order form for the source code. Appendix B contains descriptions of
additional resources in neural networks, evolutionary computation, fuzzy
systems, and computational intelligence. Included are organizations,
publications, and conferences.

----- End Included Message -----