Evidential reasoning neural networks

Author

Mohiddin, S.M. ; Dillon, T.S.

Author_Institution

Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Melbourne, Vic., Australia

Volume

3

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1600

Abstract

This paper proposes an neural network architecture for evidential reasoning. This has been achieved by combining an extended multilayered neural network for learning rules and decision trees with a new interpretation. The new interpretation of the decision tree converts a decision tree into an evidential reasoning construct called hierarchy tree (HT). Fuzzy knowledge representation methods have been used in the HTs for approximate reasoning. Fusing the HT into a neural network it is shown that imprecision and ignorance can be handled

Keywords

case-based reasoning; decision theory; feedforward neural nets; fuzzy neural nets; knowledge representation; trees (mathematics); uncertainty handling; approximate reasoning; decision trees; evidential reasoning; fuzzy knowledge representation; hierarchy tree; learning rules; multilayered neural network; neural network architecture; Classification tree analysis; Computer architecture; Computer science; Decision trees; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Knowledge representation; Labeling; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374395

Filename

374395