Advertisement

Approximate Reasoning

  • James J. Buckley
  • Esfandiar Eslami
Part of the Advances in Soft Computing book series (AINSC, volume 13)

Abstract

A method of processing information (data) through fuzzy rules is called approximate reasoning. If we have only one fuzzy rule like “if size is big, then speed is slow”, and we are given a (fuzzy) value for size, then approximate reasoning gives us a method of computing a conclusion about speed. The terms “big”, “slow” and the data for “size” are all represented as fuzzy sets. The single rule case is discussed in the next section and multiple fuzzy rules are studied in the third section. Also in the third section of this chapter we look at two methods of evaluating a block of fuzzy rules: (1) FITA, or first infer and then aggregate; and (2) FATI, or first aggregate and then infer.

Keywords

Fuzzy Number Fuzzy Rule Linguistic Variable Classical Logic Triangular Fuzzy Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • James J. Buckley
    • 1
  • Esfandiar Eslami
    • 2
  1. 1.Mathematics DepartmentUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Department of MathematicsShahid Bahonar UniversityKermanIran

Personalised recommendations