Modelling Radial Basis Functions with Rational Logic Rules

Authors:
Davide Sottara;Paola Mello
Affiliations:
Department of Electronics, Computer Science and Systems Faculty of Engineering, University of Bologna, Bologna (BO), Italy 40129;Department of Electronics, Computer Science and Systems Faculty of Engineering, University of Bologna, Bologna (BO), Italy 40129
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

Citing 5
Cited 0

Fuzzy logic of Lukasiewicz logic: a clarification

Fuzzy Sets and Systems
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Fuzzy Sets and Systems: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications
Extensions of belief functions and possibility distributions by using the imprecise Dirichlet model

Fuzzy Sets and Systems
Reasoning with imprecise belief structures

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

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.