A Fuzzy Relational Neural Network for Pattern Classification

  • Israel Suaste-Rivas
  • Orion F. Reyes-Galaviz
  • Alejandro Diaz-Mendez
  • Carlos A. Reyes-Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


In this paper we describe the implementation of a fuzzy relational neural network model. In the model, the input features are represented by fuzzy membership, the weights are described in terms of fuzzy relations. The output values are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm is a modified version of back-propagation. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies in recently born babies.


Automatic Speech Recognition Fuzzy Neural Network Neural Network Trainer Triangular Membership Function Linguistic Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Israel Suaste-Rivas
    • 1
  • Orion F. Reyes-Galaviz
    • 2
  • Alejandro Diaz-Mendez
    • 1
  • Carlos A. Reyes-Garcia
    • 1
  1. 1.Instituto Nacional de Astrofisica Optica y Electronica (INAOE)Mexico
  2. 2.Instituto Tecnologico de ApizacoMexico

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