Fuzzy neural-logic system

Author

Hsu, L.S. ; Teh, H.H. ; Wang, P.Z. ; Chan, S.C. ; Loe, K.F.

Author_Institution

Nat. Univ. of Singapore, Singapore

Volume

1

fYear

1992

fDate

7-11 Jun 1992

Firstpage

245

Abstract

A realization of fuzzy logic by a neural network is described. Each node in the network represents a premise or a conclusion. Let x be a member of the universal set, and let A be a node in the network. The value of activation of node A is taken to be the value of the membership function at point x, m_A(x). A logical operation is defined by a set of weights which are independent of x. Given any value of x, a preprocessor will determine the values of the membership function for all the premises that correspond to the input nodes. These are treated as input to the network. A propagation algorithm is used to emulate the inference process. When the network stabilizes, the value of activation at an output node represents the value of the membership function that indicates the degree to which the given conclusion is true. Weight assignment for the standard logical operations is discussed. It is also shown that the scheme makes it possible to define more general logical operations

Keywords

fuzzy logic; fuzzy set theory; inference mechanisms; neural nets; activation value; conclusion; fuzzy logic; inference process; logical operations; membership function; neural network; premise; propagation algorithm; weight assignment; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Inference algorithms; Neural networks; Probabilistic logic;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.287128

Filename

287128