Fuzzy relational neural network

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 l...

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Bibliographic Details
Published inInternational journal of approximate reasoning Vol. 41; no. 2; pp. 146 - 163
Main Authors Ciaramella, A., Tagliaferri, R., Pedrycz, W., Di Nola, A.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier Inc 01.02.2006
Elsevier
Subjects
Applied sciences
Artificial intelligence
Classification and approximation tasks
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Fuzzy relations
Neural networks
Neuro-fuzzy systems
Fuzzy relations
Neuro-fuzzy systems
Neural networks
Classification and approximation tasks
Backpropagation algorithm
Fuzzy relation
Learning algorithm
Recursive algorithm
Fuzzy neural nets
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Summary: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.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2005.06.016