Paper Info
Title
Modelling Radial Basis Functions with Rational Logic Rules
Abstract
Connectionist systems such as Radial Basis Function Neural Networks and similar architectures are commonly applied to solve problems of learning relations from available examples. To overcome their limits in clarity of representation, they are often interfaced with symbolic rule-based systems, provided that the information they have memorized can be interpreted. In this paper, an automatic implementation of a RBF-like system is presented using only gradual fuzzy rules learned by induction directly from training data. It is then shown that the same formalism, used with type-II truth values, can learn second-order, fuzzy relations.
Year
DOI
Venue
2008
10.1007/978-3-540-87656-4_42
HAIS
Keywords
Field
DocType
training data,symbolic rule-based system,automatic implementation,gradual fuzzy rule,available example,connectionist system,modelling radial basis functions,rational logic rules,rbf-like system,fuzzy relation,radial basis function neural,similar architecture,rule based system,second order,radial basis function
Radial basis function network,Neuro-fuzzy,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Membership function,Machine learning
Conference
Volume
ISSN
Citations 
5271
0302-9743
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Davide Sottara17214.68
Paola Mello244421.33