Treffer: Fuzzy relational neural network

Title:
Fuzzy relational neural network
Source:
Advances in fuzzy sets and rough setsInternational journal of approximate reasoning. 41(2):146-163
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
DMI, University of Salerno, 84081 Baronissi (SA), Italy
INFM Unit of Salerno, 84081 Baronissi (SA), Italy
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada
Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland
ISSN:
0888-613X
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17693697
Database:
PASCAL Archive

Weitere Informationen

In this paper a fuzzy neural network based on a fuzzy relational IF-THEN reasoning scheme is designed. To define the structure of the model different t-norms and t-conorms are proposed. The fuzzification and the defuzzification phases are then added to the model so that we can consider the model like a controller. A learning algorithm to tune the parameters that is based on a back-propagation algorithm and a recursive pseudoinverse matrix technique is introduced. Different experiments on synthetic and benchmark data are made. Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks. The model is also compared with some other methods known in literature.