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)

Abstract

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.

Keywords

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.

References

  1. 1.
    Pal, S.K.: Multilayer Perceptron, Fuzzy Sets, and Classification. IEEE Trans. on Neural Networks 3(5), 683–697 (1992)CrossRefGoogle Scholar
  2. 2.
    Pal, S.K., Mandal, D.P.: Linguistic Recognition Systems Based on Approximated Reasoning. Information Science 61(2), 135–161 (1992)CrossRefGoogle Scholar
  3. 3.
    Pedrycz, W.: Neurocomputations in Relational Systems. IEEE Trans. on Pattern Analysis and Mach. Intelligence 13(3), 289–296 (1991)CrossRefGoogle Scholar
  4. 4.
    Wasz-Hockert, O., Lind, J., Vuorenkoski, V., Partanen, T.: Valanne, y.E.: El Llanto en el Lactante y su Significación Diagnóstica, Cientifico-Medica, Barcelona (1970)Google Scholar
  5. 5.
    Schafer, R.W., Rabiner, L.R.: Digital Representations of Speech Signals. In: Readings in Speech Recognition, pp. 49–64. Morgan Kauffmann Publishers Inc, San Mateo (1990)Google Scholar
  6. 6.
    Park, D., Cae, Z., Kandel, A.: Investigations on the Applicability of Fuzzy Inference. Fuzzy Sets and Systems 49, 151–169 (1992)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Reyes, C.A.: On the Design of a Fuzzy Relational Neural Network for Automatic Speech Recognition. Doctoral Dissertation, The Florida State University, Tallahassee, Fl. (April 1994)Google Scholar
  8. 8.
    Boersma, P., Weenink, D.: Praat v. 4.0.8. A system for doing phonetics by computer. Institute of Phonetic Sciences of the University of Amsterdam (February 2002)Google Scholar
  9. 9.
    Ekkel, T.: Neural Network-Based Classification of Cries from Infants Suffering from Hypoxia-Related CNS Damage, Master Thesis. University of Twente, The Netherlands (2002)Google Scholar
  10. 10.
    Orozco Garcia, J., Reyes Garcia, C.A.: Mel-Frequency Cepstrum Coeficients Extraction from Infant Cry for Classification of Normal and Pathological Cry with Feed-forward Neural Networks. In: Proceedings of ESANN 2003, Bruges, Belgium (2003)Google Scholar
  11. 11.
    Bandler, W., Kohout, L.J.: Fuzzy Relational Products as a tool for analysis and artificial systems. In: Wang, P.P., Chang, S.K. (eds.) Fuzzy Sets: Theory and Applications to Policy Analysis and Information Systems, pp. 341–367. Plenum Press, New York (1980)Google Scholar

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

Personalised recommendations