Interpolative reasoning in fuzzy logic and neural network theory

Author

Zadeh, Lotfi A.

Author_Institution

Electron. Res. Lab., California Univ., Berkeley, CA, USA

fYear

1992

fDate

8-12 Mar 1992

Firstpage

1

Abstract

Summary form only given. Interpolative reasoning plays a key role in both fuzzy logic and neural network theory. The basic approaches to interpolative reasoning in both fuzzy logic and neural networks were surveyed, and their differences and similarities were analyzed. An important issue in interpolative reasoning in fuzzy logic relates to the solution of a system of fuzzy algebraic equations. Various approaches to this problem, including fuzzy Lagrangian interpolation and the use of FA-Prolog, were described and analyzed. Among other issues discussed were the compression of a system of fuzzy if-then rules and the induction of rules from observations

Keywords

data compression; fuzzy logic; inference mechanisms; interpolation; neural nets; FA-Prolog; data compression; fuzzy Lagrangian interpolation; fuzzy algebraic equations; fuzzy if-then rules; fuzzy logic; interpolative reasoning; neural network; rule induction; Computer science; Equations; Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Inference algorithms; Intelligent networks; Interpolation; Lagrangian functions; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1992., IEEE International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0236-2

Type

conf

DOI

10.1109/FUZZY.1992.258757

Filename

258757