Paper Info
Title
Application of Connectionist Models to Fuzzy Inference Systems
Abstract
In this paper, we will try to shed light on the usefulness of neural networks by describing an application which combines connectionism and ruled-based systems. In the present fuzzy ruled production systems, propagation of uncertainty coefficients is carried out by means of computational formulae stemming from mathematical models of fuzzy reasoning. But the use of a formula provided by a general abstact model, and not intimately related to the application, can lead us to a fuzzy procedure not reflecting the fuzzy reasoning of the human expert. The connectionist approach proposed here solves this problem of fuzzy inference. An uncertainty propagation rule specific to the application domain is determined by learning from examples of fuzzy inferences.
Year
DOI
Venue
1990
10.1007/3-540-55425-4_16
Dagstuhl Seminar on Parallelization in Inference Systems
Keywords
Field
DocType
connectionist models,fuzzy inference systems,production system,mathematical model,neural network,rule based system,uncertainty propagation
Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy control system,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Machine learning
Conference
Volume
ISSN
ISBN
590
0302-9743
3-540-55425-4
Citations 
PageRank 
References 
1
0.55
2
Authors
2
Name
Order
Citations
PageRank
Claude F. Touzet1699.09
Norbert Giambiasi222737.59