Skip to main content

The Fuzzy Systems Handbook

A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems

  • 2nd Edition - October 14, 1998
  • Latest edition
  • Author: Earl Cox
  • Language: English

This new edition provides a comprehensive introduction to fuzzy logic, and leads the reader through the complete process of designing, constructing, implementing, verifying and… Read more

Purchase options

Sorry, this title is not available for purchase in your country/region.

WINTER SALE

Discover your season of innovation

Up to 25% off books, eBooks and Journals

This new edition provides a comprehensive introduction to fuzzy logic, and leads the reader through the complete process of designing, constructing, implementing, verifying and maintaining a platform-independent fuzzy system model. The book has been extensively revised to bring the subject up-to-date, and features two new chapters: "Building and Using Fuzzy Cognitive Map Models" and "Building ME-OWA Models."

The multiplatform CD-ROM contains all the C++ source code from the book's examples - but its real value is the robust package of fuzzy system related tools and utilities, featuring two notable components. First: Metus Systems' basic fuzzy modeling software, which includes complete C/C++ source code for creating and executing fuzzy models, a Visual Basic shell that can be used to create fuzzy sets and generate the C/C++ include files, and code for models for pricing, project management, risk assessment, and more. Second: The ME-OWA (Minimum-Entropy, Ordered Weighted Aggregation) decision modeling software from Fuzzy Logic, Inc. This software is used to focus on a single objective function from a set of alternatives given a fuzzy ranking among various alternatives. It is not only an important technique as a stand-alone tool, but is an important methodology in parameter selection (and parameterization ordering) for genetic algorithms and various data mining techniques. It is also an important technique used to establish rule and policy level peer weights in fuzzy models.