Fuzzy relational neural network

  • Authors:

  • A. Ciaramella;R. Tagliaferri;W. Pedrycz;A. Di Nola

  • Affiliations:

  • DMI, University of Salerno, 84081 Baronissi (SA), Italy and INFM Unit of Salerno, 84081 Baronissi (SA), Italy;DMI, University of Salerno, 84081 Baronissi (SA), Italy and INFM Unit of Salerno, 84081 Baronissi (SA), Italy;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada AB T6G 2G6 and Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland;DMI, University of Salerno, 84081 Baronissi (SA), Italy

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2006

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Abstract

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.